Detonation Structure in Ethylene/Air-Based Non-Premixed Rotating Detonation Engine
Not provided.
Engineering↗
SEARCH · Engineering Papers
Results for “system engineering system process”
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.
Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML
With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.
Di Guglielmo, Giuseppe [Fermilab]↗
Technical Assistance for Remote Sensor Placement Related Projects for Structural Health Monitoring
The world’s infrastructure is aging and many aspects are cost prohibitive to replace or even repair unless critically necessary. The standard way of determining if these repairs are critical or not is usually dependent on trained visual individual inspection. This process is very time consuming and costly considering the large areas and various structures that need to be monitored. A cost effective solution to this problem is to be able to monitor structures, such as roads, buildings, and bridges with remote sensors. Developing this solution provides some unique challenges such as sensor placement, types of sensors, energy needs for placed sensors, and data retrieval. As for the aspect of sensor placement, one method is to fly semi-autonomous Unmanned Arial Vehicles (UAVs) loaded with a sensor deployment mechanism to the structures so as to place the sensor and perhaps retrieve data from previously placed sensors as well. Many tests need to be run to determine the best UAV structure and deployment system to fulfill this task. It is hoped that through a multidisciplinary engineering scope the most effective process of monitoring the aging infrastructure can be developed.
42 ENGINEERING↗
Integrated bioprocess for conversion of gaseous substrates to liquids
In the quest for inexpensive feedstocks for the cost-effective production of liquid fuels, we have examined gaseous substrates that could be made available at low cost and sufficiently large scale for industrial fuel production. In this work, we introduce a new bioconversion scheme that effectively converts syngas, generated from gasification of coal, natural gas, or biomass, into lipids that can be used for biodiesel production. We present an integrated conversion method comprising a two-stage system. In the first stage, an anaerobic bioreactor converts mixtures of gases of CO 2 and CO or H 2 to acetic acid, using the anaerobic acetogen Moorella thermoacetica . The acetic acid product is fed as a substrate to a second bioreactor, where it is converted aerobically into lipids by an engineered oleaginous yeast, Yarrowia lipolytica . We first describe the process carried out in each reactor and then present an integrated system that produces microbial oil, using synthesis gas as input. The integrated continuous bench-scale reactor system produced 18 g/L of C16-C18 triacylglycerides directly from synthesis gas, with an overall productivity of 0.19 g•L -1 •h -1 and a lipid content of 36%. Although suboptimal relative to the performance of the individual reactor components, the presented integrated system demonstrates the feasibility of substantial net fixation of carbon dioxide and conversion of gaseous feedstocks to lipids for biodiesel production. The system can be further optimized to approach the performance of its individual units so that it can be used for the economical conversion of waste gases from steel mills to valuable liquid fuels for transportation.
09 BIOMASS FUELS↗
Enterprise: a reduced-scale, flexible fuel, single-cylinder crosshead marine diesel research engine
A custom-built 1/10 scale, flexible-fuel, single-cylinder crosshead marine diesel research engine, dubbed the Enterprise, was designed and built as a platform for conducting marine lubricant research. The engine design was focused on maintaining lubricant boundary conditions similar those of full-scale engines. The Enterprise has a rated speed of 625 rpm to match the linear mean piston speed of full-scale marine engines with the reduced stroke and is capable of 18 bar brake mean effective pressure (BMEP). A high-swirl, uniflow-scavenged, two-stroke combustion system was designed to operate on either distillate diesel or heavy fuel oil (HFO). A hydraulic electronic unit injector (HEUI) fuel injection system with two injectors having customized three-hole nozzles provides fueling and is driven by the MAHLE Flexible ECU (MFE). Intake air boost is provided using a variable speed industrial air compressor along with closed-loop electronically controlled pressure regulation. Exhaust backpressure is also controlled electronically using an electro-pneumatic butterfly valve. A customized, miniaturized Hans Jensen Lubetronic cylinder lubrication system, which is capable of delivering 0.88 mg of cylinder oil per lubricator per injection to the ring pack at up to the maximum engine overspeed rating of 656 rpm, was commissioned for the Enterprise. Four independently temperature-controlled cooling zones are used to provide control of the liner temperature profile. The four coolant zones as well as the system oil and cylinder oil supplies are all controlled within 1°C for consistent thermal boundary conditions. The engine is instrumented with an in-cylinder pressure transducer as well as an inline torque transducer and numerous process temperature and pressure measurements to allow for detailed thermodynamic efficiency analysis, and experimental facilities allow for precise measurement of liner wear and characterization of deposits on the piston and rings. A radio telemetry system from IR Telemetrics is used to measure piston crown temperatures during engine operation. Engine design considerations and experimental facilities are described in detail, along with the results of a study investigating the impact of both system oil and cylinder oil viscosity on friction and efficiency. A test matrix was designed to compare base stock chemistries and viscosities for both system and cylinder oils. Experiments were completed using a fuel economy test protocol that was defined, consisting of repeating six speed/load operating conditions. An automated dynamometer control system was implemented to ensure repeatability of the test procedure. Preliminary testing indicated that the coolant, oil, and exhaust temperatures reach a steady state within 5 minutes of changing operating conditions. The system oil viscosity was found to have a significant impact on friction, with a reduction in viscosity from SAE 30 to SAE 20 resulting in over 6% reduction in friction mean effective pressure (FMEP) and over a 2% reduction in brake-specific fuel consumption (BSFC). Chemistry effects were also evident in the system and cylinder oils. Repeatability comparisons between the individual repetitions of each operating condition were used to ensure that results are statistically valid. For the initial cylinder oil viscosity experiments, observed changes were within the established/normal repeatability; ongoing experiments will determine whether improvements in boundary condition control will enable cylinder friction changes of this magnitude to be measured on this engine platform.
Kaul, Brian↗
Analysis of failure modes in pipe-in-pipe repair systems for water and gas pipelines
Not Available
42 ENGINEERING↗
Fitness Tracker for Weight Lifting Style Workouts
This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to those of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.
42 ENGINEERING↗
Implementation and evaluation of permeability-porosity and tortuosity-porosity relationships linked to mineral dissolution-precipitation
Changes of porosity, permeability, and tortuosity due to physical and geochemical processes are of vital importance for a variety of hydrogeological systems, including passive treatment facilities for contaminated groundwater, engineered barrier systems (EBS), and host rocks for high-level nuclear waste (HLW) repositories. Due to the nonlinear nature and chemical complexity of the problem, in most cases, it is impossible to verify reactive transport codes analytically, and code intercomparisons are the most suitable method to assess code capabilities and model performance. This paper summarizes model intercomparisons for six hypothetical scenarios with generally increasing geochemical or physical complexity using the reactive transport codes CrunchFlow, HP1, MIN 3 P, PFlotran, and TOUGHREACT. Benchmark problems include the enhancement of porosity and permeability through mineral dissolution, as well as near complete clogging due to localized mineral precipitation, leading to reduction of permeability and tortuosity. Processes considered in the benchmark simulations are advective-dispersive transport in saturated media, kinetically controlled mineral dissolution-precipitation, and aqueous complexation. Porosity changes are induced by mineral dissolution-precipitation reactions, and the Carman-Kozeny relationship is used to describe changes in permeability as a function of porosity. Archie’s law is used to update the tortuosity and the pore diffusion coefficient as a function of porosity. Results demonstrate that, generally, good agreement is reached amongst the computer models despite significant differences in model formulations. As a result, some differences are observed, in particular for the more complex scenarios involving clogging; however, these differences do not affect the interpretation of system behavior and evolution.
58 GEOSCIENCES↗
Contingency Analysis Post-Processing With Advanced Computing and Visualization
Contingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation caused by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly in order to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their work efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a WECC planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.
Chen, Yousu↗
Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model
Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.
Artifical Intelligence↗
Ammonia Generation over TWC for Passive SCR NOX Control for Lean Gasoline Engines
A commercial three-way catalyst (TWC) was evaluated for ammonia (NH3) generation on a 2.0-liter BMW lean burn gasoline direct injection engine as a component in a passive ammonia selective catalytic reduction (SCR) system. The passive NH3 SCR system is a potential low cost approach for controlling nitrogen oxides (NOX) emissions from lean burn gasoline engines. In this system, NH3 is generated over a close-coupled TWC during periodic slightly rich engine operation and subsequently stored on an underfloor SCR catalyst. Upon switching to lean, NOX passes through the TWC and is reduced by the stored NH3 on the SCR catalyst. NH3 generation was evaluated at different air-fuel equivalence ratios at multiple engine speed and load conditions. Near complete conversion of NOX to NH3 was achieved at =0.96 for nearly all conditions studied. At the =0.96 condition, HC emissions were relatively minimal, but CO emissions were significant. Operation at AFRs richer than =0.96 did not provide more NH3 yield and led to higher HC and CO emissions. Results of the reductant conversion and consumption processes were used to calculate a representative fuel consumption of the engine operating with an ideal passive SCR system. The results show a 1-7% fuel economy benefit at various steady-state engine speed and load points relative to a stoichiometric engine operation.
Prikhodko, Vitaly Y↗
Sensor System and Observer Algorithm Co-Design For Modern Internal Combustion Engine Air Management Based on H2 Optimization
This paper outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on H 2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the H 2 norm of the observer error and the weighted l 1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited engine using exhaust gas circulation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best H 2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.
Zhang, Xu↗
Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states
Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.
42 ENGINEERING↗
Materials Engineering and Scale Up of Fluid Phase Chemical Hydrogen Storage for Automotive Applications
Among candidates for chemical hydrogen storage in PEM fuel cell automotive applications, ammonia borane (AB, NH3BH3) is considered to be one of the most promising materials due to its high hydrogen content of 14-16 wt% below 200°C and high volumetric density. In our previous paper, we selected AB in silicone oil as a role model for a slurry hydrogen storage system. Materials engineering properties were optimized by increasing solid loading by using an ultra-sonic process. In this paper, we proceeded to scale up to liter size batches with solid loadings up to 50 wt% (8 wt% H2) with dynamic viscosities less than 1000cP at 25°C. The use of a non-ionic surfactant, Triton X-15, shows significant promise in controlling the level of foaming produced during the thermal dehydrogenation of the AB. Through the development of new and efficient processing techniques and the ability to adequately control the foaming, stable homogenous slurries of high solid loading have been demonstrated as a viable hydrogen delivery source.
ammonia borane↗
UV-enhanced CO sensing using Ga 2 O 3 -based nanorod arrays at elevated temperature
Monitoring and control of gaseous combustion process are critically important in advanced energy systems such as power plants, gas turbines, and automotive engines. However, very limited gas sensing solutions are available in the market for such application due to the inherent high temperature of combustion gaseous atmosphere. In this study, we fabricated and demonstrated high-performance metal oxide based nanorod array sensors assisted with ultra-violet (UV) illumination for in situ and real-time high-temperature gas detection. Without UV-illumination, it was found surface decoration of either 5 nm LSFO or 1 nm Pt nanoparticles can enhance the sensitivity over CO at 500 °C by an order of magnitude. Under the 254 nm UV illumination, CO gas-sensing performance of Ga 2 O 3 -based nanorod array sensors was further enhanced with the sensitivity boosted by 125 %, and the response time reduced by 30 % for La 0.8 Sr 0.2 FeO 3 (LSFO)-decorated sample. The UV-enhanced detecting of CO might be due to the increased population of photo-induced electron-hole pairs. While for LSFO-decorated nanorod array sensor under UV illumination, the enhancement is through a combination of sensitizing effect and photocurrent effect.
36 MATERIALS SCIENCE↗
Build redesigned HFIR rabbit capsules and make ready for insertion for irradiation in HFIR
This report details the fabrication and delivery of two Fuel Cycle Research and Development irradiation capsules (FCRP20 and FCRP03), with associated quality assurance documentation, to the High Flux Isotope Reactor. The capsules and documentation were delivered by September 30, 2015, thus meeting the deadline for milestone M3FT-15OR0203112. These irradiation experiments irradiate metal parallelepiped specimens that may consist of various compositions including uranium metal, steel, etc. This document contains a copy of the completed capsule fabrication request sheets, which detail all constituent components, pertinent drawings, etc., along with a detailed summary of the capsule assembly process performed by the Thermal Hydraulics and Irradiation Engineering Group (THIEG) in the Reactor and Nuclear Systems Division. A complete fabrication package record is maintained by THIEG and is available upon request.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Delivery of completed irradiation vehicles and the quality assurance document to the High Flux Isotope Reactor for irradiation
This report details the initial fabrication and delivery of two Fuel Cycle Research and Development (FCRD) irradiation capsules (ATFSC01 and ATFSC02), with associated quality assurance documentation, to the High Flux Isotope Reactor (HFIR). The capsules and documentation were delivered by September 30, 2015, thus meeting the deadline for milestone M3FT-15OR0202268. These irradiation experiments are testing silicon carbide composite tubes in order to obtain experimental validation of thermo-mechanical models of stress states in SiC cladding irradiated under a prototypic high heat flux. This document contains a copy of the completed capsule fabrication request sheets, which detail all constituent components, pertinent drawings, etc., along with a detailed summary of the capsule assembly process performed by the Thermal Hydraulics and Irradiation Engineering Group (THIEG) in the Reactor and Nuclear Systems Division (RNSD). A complete fabrication package record is maintained by the THIEG and is available upon request.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Requirements Document for Development of a Livermore Tomography Tools Interface
In this document, we outline an exercise performed at LLNL to evaluate the user interface deficits of a LLNL-developed CT reconstruction software package, Livermore Tomography Tools (LTT). We observe that a difficult-to-use command line interface and the lack of support functions compound to generate a bottleneck in the CT reconstruction process when input parameters to key functions are not well known. Through the exercise of systems engineering best practices, we generate key performance parameters for a LTT interface refresh, and specify a combination of back-end (“test-mode” functions) and front-end (graphical user interface visualization and command scripting tools) solutions to LTT’s poor user interface that aim to mitigate issues and lower costs associated with CT reconstruction using LTT. Key functional and non-functional requirements and risk mitigation strategies for the solution are outlined and discussed.
42 ENGINEERING↗