Search NASA⌕ Search

SEARCH · Search NASA

Results for “speed optimization”

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.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analysis of Control Behavior in Eco-Driving Speed Optimization Using Pontryagin’s Minimum Principle

The energy efficiency of autonomous vehicles can be improved by selecting an optimized speed profile. Energy savings can be maximized by performing control optimization with knowledge of the powertrain characteristics and future driving conditions. Previous studies have shown that Pontryagin’s minimum principle (PMP) performs well in vehicle speed optimization problems. Building on the methods proposed in previous studies, the contribution of this study is to derive meaningful observations from the concepts and results of PMP to enhance the understanding of the control problem. In particular, the switching behavior of the control mode is analyzed with supportive variables, such as ξ and mv, which dictates the changes in the control modes. Additionally, the existence of the singular control is analyzed, which helps in understanding the cruise driving in the control problem. Finally, we obtain several solutions that satisfy various boundary conditions along with a map of the reachable states, and discuss the impact of cruise driving. This is helpful for designing practical control concepts for real-world applications based on this map. Previous studies have contributed significantly to this control problem; however, this study provides a better understanding of the issue and offers guidance and inspiration for future real-world applications based on these meaningful observations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Audi e-tron Green Light Optimized Speed Advisory On-Road Data

To aid researchers in studying the capabilities and benefits of vehicle-to-infrastructure communication, Argonne National Laboratory collected a robust set of on-road driving data of the Audi Green Light Optimized Speed Advisory (GLOSA) system implemented in the e-tron battery electric vehicle. This dataset includes 33 tests, each roughly 27 miles in length and roughly 45 to 75 minutes in duration. The team selected Kane County Highway Route 34 from Main Street in Batavia, Illinois to Middlecreek Lane in St. Charles, Illinois as the route do to its high density of GLOSA-active lights and the most opportunities to observe the system per hour of test time. The data include parameters from the following sources: GLOSA system driving the dash indicators, multiple powertrain parameters including real-time battery power/energy consumption, GPS, front radar gap, and rear radar gap. ![audio-e-tron image](audi-e-tron.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Speed Optimizations for Physics Ray Trace Algorithms

Ray tracing is a process used commonly in computer graphics and in physics to track light photons and particles, respectively. Much research was found on improving execution times for the computer graphics applications; however, in the short time frame of this literary review, almost no research was found on improving the execution times for the physics applications that were relevant to this problem. Two ray trace algorithms, a STL raytrace and a conebeam raytrace, were optimized using OpenMP and CUDA.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Optimal speed sharing characteristics of a series-hybrid bearing

A series-hybrid bearing assembly consisting of a conical hydrostatic fluid-film bearing and a ball bearing is described. Computer studies are used to predict friction torque and life characteristics of a 150-millimeter ball bearing. A conical hydrostatic fluid-film bearing is designed for minimum friction and maximum speed reduction of the ball-bearing component of the series-hydrid bearing. At a thrust load of 4000 pounds and speeds corresponding to DN (bearing bore in millimeters times shaft speed in rpm) values of 3 and 4 million, ball-bearing speed may be reduced by 30 percent. This speed reduction corresponds to ball-bearing fatigue life improvement factors of 3.4 at 3 million DN and 5.9 at 4 million DN. An oil flow rate of 18.2 pounds per minute is required to maintain a fluid-film thickness of 0.001 inch in the hydrostatic bearing.

Nypan, L. J.↗
NASA NTRS1972

Optimal speed sharing characteristics of a series-hybrid bearing.

A series-hybrid bearing assembly consisting of a conical hydrostatic fluid-film bearing and a ball bearing is described. Computer studies are used to predict friction torque and life characteristics of a 150-mm ball bearing. A conical hydrostatic fluid-film bearing is designed for minimum friction and maximum speed reduction of the ball-bearing component of the series-hybrid bearing. At a thrust load of 4000 lb and speeds corresponding to DN (bearing bore in millimeters times shaft speed in rpm) values of 3 and 4 million, ball-bearing speed may be reduced to 30%. This speed reduction corresponds to ball-bearing fatigue life improvement factors of 3.4 at 3 million DN and 5.9 at 4 million DN. An oil flow rate at 18.2 lb/min is required to maintain a fluid-film thickness of 0.001 in. in the hydrostatic bearing.

Nypan, L. J.↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware

The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Partial Derivatives of the Lambert Problem

A procedure for deriving analytic partial derivatives of the Lambert problem is presented. Using the universal, cosine based Lambert formulation; first order partial derivatives of the velocities with respect to the positions and times are developed. Taking advantage of inherent symmetries and intermediate variables, the derivatives are expressed in a computationally efficient form. The added cost of computing these partials is found to be approximately 10% to approximately 60% of the Lambert compute cost. The availability of analytic partial derivatives increases optimization speed, efficiency and allows for trajectory optimization formulations that implicitly enforce continuity constraints via embedded Lambert problems.

trajectory computation↗
DOE OSTI2023

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗
NASA NTRS2014

An Evaluation of a Flight Deck Interval Management Algorithm Including Delayed Target Trajectories

NASA's first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature air traffic management technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise timebased scheduling in the terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools enabling precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise in-trail spacing. During high demand operations, TMA-TM may produce a schedule and corresponding aircraft trajectories that include delay to ensure that a particular aircraft will be properly spaced from other aircraft at each schedule waypoint. These delayed trajectories are not communicated to the automation onboard the aircraft, forcing the IM aircraft to use the published speeds to estimate the target aircraft's estimated time of arrival. As a result, the aircraft performing IM operations may follow an aircraft whose TMA-TM generated trajectories have substantial speed deviations from the speeds expected by the spacing algorithm. Previous spacing algorithms were not designed to handle this magnitude of uncertainty. A simulation was conducted to examine a modified spacing algorithm with the ability to follow aircraft flying delayed trajectories. The simulation investigated the use of the new spacing algorithm with various delayed speed profiles and wind conditions, as well as several other variables designed to simulate real-life variability. The results and conclusions of this study indicate that the new spacing algorithm generally exhibits good performance; however, some types of target aircraft speed profiles can cause the spacing algorithm to command less than optimal speed control behavior.

Swieringa, Kurt A.↗
NASA NTRS1990

The 4-D descent trajectory generation techniques under realistic operating conditions

NASA-Langley has been conducting and sponsoring research in airborne energy management for a number of years. During the course of this research, two fundamental techniques for the generation of 4D (fixed time) descent trajectories have emerged as viable candidates for advanced flight management systems. The first technique utilizes speed schedules of constant Mach number transitioning to constant calibrated airspeed chosen empirically to produce minimum fuel usage. The second technique computes cost optimized speed schedules of variable airspeed developed through application of optimal control theory. Both techniques have been found to produce reasonable and flyable descent trajectories. The formulation of the algorithms for each technique is evaluated and their suitability for operations in realistic conditions is discussed. Operational factors considered include: airplace speed, thrust, and altitude rate constaints; wind, temperature, and pressure variations; Air Traffic Control altitude, speed, and time constaints; and pilot interface and guidance considerations. Time flexibility, fuel usage, and airborne computational requirements were the primary performance measures.

Williams, David H.↗
NASA NTRS2015

Minimum-Cost Aircraft Descent Trajectories with a Constrained Altitude Profile

An analytical formula for solving the speed profile that accrues minimum cost during an aircraft descent with a constrained altitude profile is derived. The optimal speed profile first reaches a certain speed, called the minimum-cost speed, as quickly as possible using an appropriate extreme value of thrust. The speed profile then stays on the minimum-cost speed as long as possible, before switching to an extreme value of thrust for the rest of the descent. The formula is applied to an actual arrival route and its sensitivity to winds and airlines' business objectives is analyzed.

altitude restrictions↗
NASA NTRS1984

Aeroelastic-aerodynamic optimization of high speed helicopter-compound rotor

Several problems related to the aeroelastic/aerodynamic optimization of a high speed helicopter compound rotor are discussed. The helicopter fuselage vibration problem, the effects of fuselage vibrations, the source of external and periodic air loads, typical airfoil environments and configurations, rotor dynamics, vibration reduction, and requirements for the rotor design optimization analysis are among the topics covered.

Sutton, L. R.↗
NASA NTRS1993

Synthesis algorithm of VLSI multipliers for ASIC

Multipliers are critical sub-blocks in ASIC design, especially for digital signal processing and communications applications. A flexible multiplier synthesis tool is developed which is capable of generating multiplier blocks for word size in the range of 4 to 256 bits. A comparison of existing multiplier algorithms is made in terms of speed, silicon area, and suitability for automated synthesis and verification of its VLSI implementation. The algorithm divides the range of supported word sizes into sub-ranges and provides each sub-range with a specific multiplier architecture for optimal speed and area. The algorithm of the synthesis tool and the multiplier architectures are presented. Circuit implementation and the automated synthesis methodology are discussed.

Chua, O. H.↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗