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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

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↗
DOE OSTI2019

Convex Relaxations of the Network Flow Problem Under Cycle Constraints

In this paper, we consider the problem of optimizing the flows in a lossless flow network with additional nonconvex cycle constraints on nodal variables; such constraints appear in several applications, including electric power, water distribution, and natural gas networks. This problem is a nonconvex version of the minimum-cost network flow problem (NFP), and to solve it, we propose three different approaches. One approach is based on solving a convex approximation of the problem, obtained by augmenting the cost function with an entropy-like term to relax the nonconvex constraints. We show that the approximation error, for which we give an upper bound, can be made small enough for practical use. An alternative approach is to solve the classical NFP, that is, without the cycle constraints, and solve a separate optimization problem afterwards in order to recover the actual flows satisfying the cycle constraints; the solution of this separate problem maximizes the individual entropies of the cycles. The third approach is based on replacing the nonconvex constraint set with a convex inner approximation, which yields a suboptimal solution for the cyclic networks with each edge belonging to at most two cycles. We validate the practical usefulness of the theoretical results through numerical examples, in which we study the standard test systems for water and electric power distribution networks.

42 ENGINEERING↗
DOE OSTI2020

Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite

This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. Here, to address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.

42 ENGINEERING↗
DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗