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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

WholeTraveler Anonymized Data Phase 1

Phase 1 of the WholeTraveler Study data collection consisted of an online-only survey. This survey captured data on three categories of observable variation in the population relevant to transportation decisions. First, the survey collected traditional demographic data such as age, gender, income, and education level. Second, it collected data across personality, psychological, and preference categories. This included: 1. The "Big Five" inventory personality traits: openness to new experience, conscientiousness, extroversion, agreeableness, and neuroticism; 2. Risk and time preferences; and 3. Environmental preferences. Third, the survey collected data on historical behavior patterns including: 1. Adoption of (as well as interest in) new technologies or innovations (e.g., smartphones, PEVs, solar panels, adaptive cruise control [ACC]); 2. Car ownership history and current car ownership status; 3. Recent mode use across different time scales (e.g., previous week, previous month, previous year); and 4. Timing of major life events such as starting a family as well as overall lifecycle trajectory patterns. Data from Phase 1 and Phase 2 are linked by a unique respondent identifier. Anonymized versions of the Phase 1 and Phase 2 data are both available on Livewire.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Minimizing Ground Risk in Cellular-Connected Drone Corridors With mmWave Links

Unmanned Aircraft Systems (UASs) have been receiving significant interest and support from academia, industry, and regulatory bodies over the past decade due to their various use cases. To safely integrate UAS operations into the national airspace, particularly overpopulated regions, the risk posed to ground users, buildings, and vehicles due to unmanned aerial vehicle (UAV) flight should be minimized. This risk can be represented by a numerical metric, which we refer to in this article as the “ground risk.” Many UAS applications also depend on the presence of a reliable wireless communication link between the UAV and a control station for the transmission of UAV position, surveillance video, UAV payload commands, and other mission-related data. Such wireless communication requirements also need to be considered in the design of UAS operations. In this article, we consider both these aspects and study the design of nonintersecting trajectories for UAS operations to minimize ground risk, subject to constraints on the wireless signal strength and geometry of the trajectory, specified in terms of: 1) an enclosing cylinder within which the trajectory must lie and 2) an integrated angular change along the UAV's trajectory. The performance of a computationally expensive optimal algorithm is compared with that of a computationally faster heuristic approach within the dense urban environment of Manhattan, NY, USA. Performance evaluation using ray-tracing simulations shows that the heuristic approach performs close to the optimal algorithm at a reduced computation cost. In conclusion, this research can be utilized to make UAS operations safe and reliable and accelerate their adoption.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Joint Optimization for Transport and Bucket Loading Phases of Automated Wheel Loaders

This article investigates optimization of fuel-efficiency and productivity for automated wheel loaders. A control-oriented model for both the transport phase and bucket loading phase is proposed. Here, the vehicle model includes an automatic gear shift schedule that can be incorporated into the optimization problem. Based on the model, the multistage optimization problem is formulated to simultaneously consider all phases of a short cycle with physical constraints. Cycle time and fuel efficiency are used as the weighted performance indexes in a multiobjective cost function. Bucket fill factor is included as a constraint during the bucket loading phase. A nonlinear programming problem is created with collocation using MATLAB and CasADi. The optimization solver IPOPT solves the problem to obtain the optimal state and control trajectories, which can be used as a reference for automated wheel loaders or even as a driver advisory for human-driven wheel loaders.

42 ENGINEERING↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2022

Multistage Stabilized Continuation for Indirect Optimal Control of Three-Dimensional Hypersonic Trajectories

This work presents the development of a multistaged stabilized continuation for the three-dimensional unpowered hypersonic trajectory planning problem using indirect optimal control methods. The stabilized continuation method is noniterative and guaranteed to terminate within a finite number of floating point operations, thereby making it well suited for onboard autonomous implementations. Here, we present a multistage formulation of the stabilized continuation scheme that involves starting with a “loose” integration tolerance during the first stage and ramping up toward a “strict” integration tolerance through subsequent stages. An important benefit of this approach is that even when the solution to the underlying optimal control problem is numerically unstable, such as with the hypersonic vehicle footprint generation problem, the stabilized continuation algorithm is shown to be successful in finding a solution while providing some additional insights into the underlying cause of the numerical instability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Knock Mitigation Effectiveness of EGR across the Pressure-Temperature Domain

Exhaust gas recirculation (EGR) has been shown to enable efficiency improvements in SI engines through multiple different mechanisms, including decreasing the knock propensity at high load, which allows higher compression ratio. While many of the benefits of EGR are applicable to both low and high power density engines, including reductions in pumping work and improved specific heat ratio, the knock benefits and corresponding compression ratio increases have been limited to low power density naturally aspirated engines primarily intended for hybrid vehicle architectures. An earlier study [1] indicated that there may be a kinetic limitation for the ability of EGR to mitigate knock under these conditions, but that study only considered a small number of conditions. In this investigation, we expand on that study while also providing data for model validation for the new light-duty combustion consortium from the U.S. Department of Energy: Partnership for Advancing Combustion Engines (PACE). In this investigation, the effectiveness of EGR to mitigate knock is studied with regards to the effect of engine speed (1,500 and 3,000 rpm), changing trajectory in the pressure-temperature domain by varying the intake manifold temperature (35, 60, and 90 deg C), and by considering the effect of minor species by studying the effect of untreated EGR vs. EGR that has been treated by an automotive three-way catalyst. Additionally, to increase the relevance of these data for future modeling studies, the performance of the full boiling range gasoline was compared relative to a surrogate formulation. The study found that the fuel surrogate performs well, confirmed the kinetic limitations of EGR to mitigate knock under boost, and showed improvements in EGR performance with catalyzed EGR.

42 ENGINEERING↗
DOE OSTI2020

Magnetic Navigation for GPS-Denied Airborne Applications

Most current flight systems are dependent on GPS for navigation. Recently, however, navigation in GPS-denied environments has become an area of intensive research. Additional navigation sensor data can be obtained from visual observations (stars or terrain), inertial measurement units, radar, measurements of the local magnetic field, or perhaps even gravity. Absolute and relative positioning via magnetic field measurements have been shown to be viable in many applications including ground navigation, low altitude aircraft flight, and spaceflight. There is greater variability in the magnetic field over shorter distances when flying at low altitude and in ground applications, leading to more accurate positioning. However, ground-based magnetic navigation is often heavily influenced by man-made structures, especially in urban environments. This is not the case for airborne magnetic navigation since the influence of buildings, roads, etc. is negligible for typical aircraft altitudes. For absolute magnetic navigation, the positioning accuracy decreases as altitude increases for a given vehicle velocity, but the observed time variability in the field can be reclaimed by traveling faster through the field. Thus, navigation accuracy becomes a balance of speed and altitude since the higher altitude can be counterbalanced by higher velocity. To understand these effects quantitatively, we explored various techniques to aid a simulated inertial measurement unit with magnetic information. Using a technique known as two-dimensional magnetic map matching, we simulated the performance of airborne magnetic navigation at fixed speed while varying the altitude, flight direction, magnetometer data collection time, reference magnetic map bias error, and type of trajectory (over land or over ocean).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Non-uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases

The ability to non-uniformly weight the input space is desirable for many applications, and has been explored for space-filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, here we apply non-uniform sampling methods for the construction of aerodynamic databases. This paper combines non-uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed-form solution to a non-uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.

42 ENGINEERING↗
DOE OSTI2024

Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link-Based Model Predictive Control

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly.

large-scale simulation↗
DOE OSTI2026

New Norms or Old Habits: Evaluating Interlinked Trajectories of Online Shopping and Work Commute Post-Pandemic

The COVID-19 pandemic has significantly shifted travel behaviors, with major changes observed in online shopping and travel to work. Despite considerable research into pandemic-induced changes in travel behavior, it remains uncertain whether these new patterns have persisted or reverted to pre-pandemic norms. This study addresses this uncertainty by evaluating whether shifts in online shopping and work travel during the pandemic have become permanently ingrained in individuals' daily routine. Leveraging data from the 2022 National Household Travel Survey, a bivariate ordered probit model is employed to analyze changes in online shopping and work travel - whether they have increased, decreased, or remained stable compared to pre-pandemic levels across different population segments. The analysis finds that the pandemic did not significantly alter online shopping for home delivery and travel to work for the majority of society. However, a substantial portion of respondents reported increased online shopping for home delivery and reduced travel to work compared to pre-pandemic levels, with online shopping trends appearing more permanent. Segment-wise analysis and model results indicate heterogeneity in behavioral shifts with females engaging more in online shopping, while zero-vehicle households are traveling less to work, compared to pre-pandemic levels. Additionally, increase in online shopping frequency is significantly and negatively correlated with decrease in traveling to work. These findings highlight the need for improved digital infrastructure, flexible work policies, and integrated transportation solutions tailored to evolving demographic and socioeconomic needs in the post-pandemic era. Additionally, the study calls for integrating passenger and freight movement in a single framework rather than treating them in silos.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Design, Selection, and Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking

Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.

Engineering↗
DOE OSTI2023

Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.

Yang, Hung-Chia (ORCID:0000000161439904)↗
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Dynamically Collected Local Density using Low-Cost Lidar and its Application to Traffic Models

This article demonstrates the use of traffic density observations collected dynamically in the vicinity of probe vehicles. Fixed position sensors cannot capture the longitudinal evolution of local traffic density in the corridor. In this research, dynamic traffic density observations were collected in a naturalistic driving setting that was free of any controlled experiment biases. Speed from global positioning system and space headway from a light detection and ranging module was collected on one arterial and one freeway segment, 2 and 4mi long, respectively. The combined data frequency was approximately 3Hz. Space headway was used to estimate the local density and consequently to identify the density of a specific location in a corridor. Besides, driver behavior was characterized using the relationship between instantaneous speed and local density under different regimes of the Wiedemann car-following model. Macroscopic traffic stream models were used to investigate the relationship between dynamically collected instantaneous speed and local density. Using the longitudinal evolution of density, precise local density across the corridor can be obtained along with the leader and follower trajectories. A method to identify driver behavior across density ranges was developed for different facility types using a microscopic relationship between instantaneous speed and local density. Overall driving behavior on the freeway segment can be represented by translating the instantaneous speed and local density relationship to macroscopic stream models.

Engineering↗
DOE OSTI2021

Validation of the HyRAM+ physics models for use with propane

The Hydrogen Risk Assessment Model Plus (HyRAM+) toolkit combines quantitative risk assessment with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. HyRAM+ is differentiated from HyRAM in that it includes models and leak data for other alternate fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for propane which is being used as a surrogate for autogas, which is a mixture of propane and butane and used in internal combustion engines in vehicles. For flame length comparisons, five previously published experiments from peer reviewed journals were used to validate our models. The validation looked at flame lengths and flame widths with respect to different leak diameters, mass flow rates, and source pressures. Most of the sources included more than one set of experimental data, which were collected using different methods (CCD cameras, IR visualization etc.). In general, HyRAM+ overpredicts the flame lengths by around 65%. For heat and radiation models, we compared the heat flux and radiation data reported from two different sources to the values calculated by HyRAM+. For higher mass flow rates, the HyRAM+ calculated flame length results gave a better estimate of what is found in the experiments (65% error), but a higher error (85%) is observed between the HyRAM+ calculated lengths and the experimental flame lengthsfor lower mass flows. Some differences can be attributed to outdoor environmental effects (i.e. wind speed) and uncertainties in jet flame shapes. The propane flame trajectory is predicted for a high Reynolds number case with Re = 12,500 and a low Reynolds number case where Re = 2,000. The Re=12,500 case which is momentum dominated matches well with the experimental flame trajectory, but the agreement for the bouancy driven low Reynolds number case is not as good. Dispersion modeling for unignited propane was also analyzed. We compared the mole fraction, mixture fraction, mean velocity, concentration half width, and inverse mass concentration over an axial distance from different credible journals to the values calculated by HyRAM+. The results display good agreement but generally, HyRAM+ predicts a wider profile for mole fraction and mixture fraction experiments. Overall, HyRAM+’s results are reasonable for predicting the flame length, heat flux, flame trajectory, and dispersion for propane and can be used in risk analyses

08 HYDROGEN↗
DOE OSTI2022

Energy‐aware relay positioning in flying networks

Summary The ability to move and hover has made rotary‐wing unmanned aerial vehicles (UAVs) suitable platforms to act as flying communications relays (FCRs), aiming at providing on‐demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on‐board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: Communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy efficiency from the communications point of view, overlooking the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion. In this article, we propose energy‐aware relay positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy‐efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the quality of service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation.

Rodrigues, Hugo↗
DOE OSTI2020

Pressure-temperature effects on knock with iso-octane and propane at various compression ratios and intake temperatures

Knock remains one of the main limitations for increased internal combustion engine efficiency. Recent trends in light-duty vehicles towards downsized, boosted engines increases the need for improving the understanding between fuel chemistry and thermodynamic effects contributing to knock. Previous studies have shown the importance of end-gas thermodynamic conditions on knock onset and behavior, with relationships to fuel chemistry illustrated. However, a complete understanding of how fuels allow access to higher engine loads and the governing physics behind end-gas knock under a wide range of thermodynamic conditions is still unclear. Experiments in this work improve this understanding with the use of three fuels (1) iso-octane, a low octane sensitivity (OS) fuel (2) a Co-Optima aromatic core fuel, which has similar research octane number (RON) yet significantly higher OS, and (3) propane, known for its knock resistance. Engine load sweeps are conducted with each fuel while maintaining a CA50 of 8 crank angle degrees after top dead center (°CA aTDCf). As load increases and knock onset is observed, spark is delayed to its knock limited spark advance (KLSA) allowing further increases in load until either one of two limits is reached; (1) CA50 retard limit (2) Peak cylinder pressure limit. Experiments are conducted at 40°C and 90°C intake temperature and at two distinct compression ratios (rc) 9.2:1 and 13.6:1. Two-zone zero-dimensional simulations were performed in Chemkin to extract end-gas pressure and temperature conditions through the combustion process for each experimental condition of interest. CA50 response as a function of engine load is compared for all experimental conditions and fuels, and a pressure-temperature (PT) trajectory analysis is conducted using constant volume ignition delay contours to explain the behavior of each fuel.

Dal Forno Chuahy, Flavio↗