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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CMOS-Based Single-Cycle in-Memory XOR/XNOR

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2× improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Modeling and Analysis of a Novel Offshore Binary Species Free-Floating Longline Macroalgal Farming System

The investigation of innovative macroalgal cultivation is important and needed to optimize farming operations, increase biomass production, reduce the impact on the ecosystem, and lower system and operational costs. However, most macroalgal farming systems (MFSs) are stationary, which need to occupy a substantial coastal area, require extensive investment in farm infrastructure, and cost high fertilizer and anchoring expenses. This study aims to model, analyze, and support a novel binary species free-floating longline macroalgal cultivation concept. The expected outcomes could provide a basis for the design and application of the novel MFS to improve biomass production, decrease costs, and reduce the impact on the local ecosystem. In this paper, Saccharina latissima and Nereocystis luetkeana were modeled and validated, and coupled with longline to simulate the binary species MFS free float in various growth periods and associated locations along the US west coast. Further, the numerical predictions indicated the possibility of failure on the longline and breakage at the kelp holdfasts is low. However, the large forces due to an instantaneous change in dynamic loads caused by loss of hydrostatic buoyancy when the longline stretches out of the water would damage the kelps. Buoy-longline contact interactions could damage the buoy, resulting in the loss of the system by sinking. Furthermore, the kelp-longline and kelp-kelp entanglements could potentially cause kelp damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structure of the Majorana Clifford group

In quantum information science, Clifford operators and stabilizer codes play a central role for systems of qubits (or qudits). In this study, we study their analogs for systems composed of Majorana fermions. In this case, a crucial role is played by fermion parity symmetry, which is an unbreakable symmetry present in any system with fundamentally fermionic degrees of freedom. We prove that the subgroup of parity-preserving Majorana Cliffords can be represented by the orthogonal group over the binary field 𝔽 2 , and we show how it can be generated by braiding operators and used to construct any (even-parity) Majorana stabilizer code. We also analyze the frame potential for this so-called p-Clifford group when acting on a fixed-parity sector of the Hilbert space, proving that it is equivalent to the frame potential of the ordinary Clifford group acting on the same sector.

Computational complexity↗
DOE OSTI2025

Lattice distortions and metal-insulator transition in hexagonal FeS

Using total x-ray scattering and density functional theory calculations based on experimental data, we study the metal-insulator transition (MIT) in the strongly correlated system FeS with hexagonal symmetry. We find that it arises from the combined effect of static lattice distortions and antiferromagnetic ordering, while electron-electron correlations appear to play a minor role. The presence of local lattice distortions above the MIT explains well the evolution of transport and magnetic properties with changing temperature or pressure. Intrinsically, lattice distortions in hexagonal FeS act as lattice degrees of freedom that bridge competing electronic phases, thus facilitating transitions between them. The mechanism of MIT operating in h-FeS appears to be common to strongly correlated binary systems involving 3d transition metals, warranting further investigations on the lattice distortions-property relationship in this broad class of materials using the approach adopted here.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Resilient Distribution System Restoration with Equitable Load Shedding

A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Gas-Gap Calorimeter Sizing and Rating Tools for Heat Pipe Experimentation

Effective gas-gap calorimeter sizing and rating tools are required to design calorimeters that enable well-defined operating conditions, high cooling powers, and accurate calorimetry. The present report describes two tools utilizing Microsoft Excel and MathWorks MATLAB that enable the sizing and rating of gas-gap calorimeters with a He-Ar binary gas mixture in the gap. The Excel tools are two easy-to-use spreadsheets for calculating heat pipe operating temperature or He-Ar mole fractions given the required inputs. The MATLAB tool includes similar models with more robust solution algorithms along with sub-options for full-vacuum, static gas-gap, and forced circulation in the gas-gap. The accuracy of the developed tools were demonstrated by comparing with existing work in literature. The MATLAB tool was used for a parametric study to determine the gas-gap thickness, coolant gap thickness, coolant flow rate, and coolant inlet temperatures for the testing of a 3/4 in outer diameter heat pipe up to powers of 10 kW and temperatures of 1,000°C. The effects of heat pipe and calorimeter inner shell surface emissivities were investigated, considering the inability to control or accurately measure surface emissivities in most applications. It was found that controlling the He-Ar mole fractions provides a wide operating range for gas-gap thicknesses around ~ 0.042--0.090 in (~ 1.07--2.29 mm) for a surface emissivity range of 0.4--0.8, since conduction heat transfer is a significant fraction of the heat transfer across the gas-gap. In addition, it was found that turbulent flow in the coolant gap is needed at high input powers to prevent shell temperatures from approaching the boiling temperature of the water coolant. The parametric study resulted in choosing stainless steel tubes with an outer diameter and thickness of 1 x 0.065 in as the inner shell, and a 1-1/2 x 0.156 in as the outer shell of the calorimeter. Overall, this report presents the necessary information for the sizing and rating of gas-gap calorimeters with He-Ar mixtures for the testing of high-temperature heat pipes (~ 500--1,000°C).

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗
DOE OSTI2022

An experimental and modeling study on autoignition of 2-phenylethanol and its blends with n-heptane

2-Phenylethanol (2-PE) is an aromatic alcohol with high research octane number, high octane sensitivity, and a potential to be produced using biomass. Considering that 2-PE can be used as a fuel additive for boosting the anti-knocking quality of gasoline in spark-ignition engines and as the low reactivity fuel or fuel component in dual-fuel reactivity controlled compression ignition (RCCI) engines, it is of fundamental and practical interest to understand the autoignition chemistry of 2-PE, especially at low-to-intermediate temperatures (<1000 K). Based upon the experimental ignition delay time (IDT) results of neat 2-PE obtained from our previous rapid compression machine (RCM) investigation and the literature shock tube study, a detailed chemical kinetic model of 2-PE is developed herein, covering low-to-high temperature regimes. Besides, RCM experiments using binary fuel blends of 2-PE and n-heptane (nC7) are conducted in this work to investigate the nC7/2-PE blending effects, as they represent a dual-fuel system for RCCI operations. Furthermore, the newly developed 2-PE model is merged with a well-validated nC7 kinetic model to generate the current nC7/2-PE binary blend model. Overall, the consolidated model reasonably predicts the experimental IDT data of neat 2-PE and nC7/2-PE blends, as well as captures the experimental effects of pressure, equivalence ratio, and blending ratio on autoignition. Finally, model-based chemical kinetic analyses are carried out to understand and identify the controlling chemistry accounting for the observed blending effects in RCM experiments. In conclusion, the analyses reveal that nC7 enhances 2-PE autoignition via providing extra $\dot{O}$H radicals to the shared radical pool, while the diminished nC7 promoting effect on 2-PE autoignition with increasing temperature is due to the negative temperature coefficient characteristics of nC7.

2-phenlyethanol↗
DOE OSTI2023

Exploration of Heat-Driven Ejector High-Temperature Heat Pumps

Heat-driven ejector heat pumps use a supersonic ejector as a thermo-compressor to replace the mechanical compressor. Supersonic ejectors have many advantages in high-temperature heat pump (HTHP) applications, including high operating temperature tolerance, no need for lubrication, low maintenance, and low cost. The coefficient of performance of ejector HTHPs could be improved by selecting binary fluids with unique thermodynamic properties. Although supersonic ejectors have been widely used in refrigeration systems, their application in spaces and water heating is limited. This study explores the theoretical potential of ejector HTHPs with a sink temperature of 100°C–130°C and a lift temperature of 10°C–30°C. Ejector HTHPs were evaluated with single-fluid ejectors (SFEs) and binary-fluid ejectors (BFEs). A comprehensive, geometry-free theoretical model of BFEs was developed to predict the theoretical maximum entrainment ratios. HFE7500 and R718 (water) were selected as working fluids for SFEs and BFEs. SFEs operating with R718 provided a higher coefficient of performance of ejector HTHPs than SFEs operating with HFE7500 and BFEs. This study preliminarily demonstrates the technical potential of ejector HTHP applications in recovering moderate-temperature heat sources

Wang, Pengtao↗
DOE OSTI2023

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Programmable pseudo-random sequence generator for use with universal lidar and its associated method of operation

A pseudo-random sequence generator for use within a universal lidar system and its corresponding method of operation. The pseudo-random sequence generator uses synchronized shift registers that are in series Binary adders are provided. The signal output of each of the shift registers is tapped and directed to the binary adders. High-speed switches are provided between the shift registers and the binary adders. The switches are programmed to connect only two of the shift registers to the binary adders for each of the pseudo-random patterns being generated. The binary adders generate an output signal that is received by the first shift register. The signal propagates through all the shift registers to the last shift register. The last shift register outputs a pseudo-random sequence.

Datta, Shubhashish↗
DOE OSTI2023

Fundamental study of gas species transport in the oxygen electrode of solid oxide fuel and electrolysis cells

A fundamental analysis of multicomponent gas transport models was performed in application to the oxygen electrodes of solid oxide cells. It is common practice to neglect the effect of pressure gradients within oxygen electrodes, even though a net molar flux at the electrolyte surface implies that a pressure gradient must exist. The influence of both Darcy velocity and Knudsen flux are considered in the context of ordinary (Fickian) diffusion, the dusty gas model, and the binary friction model. Comparisons between the models and different sets of assumptions are made via parametric studies on operating load, oxygen partial pressure, microstructural properties, and electrode thickness. Results show that the pressure gradient will have a significant impact on the oxygen concentration distribution and therefore the concentration overpotential. In electrolysis mode, pressure increases up to 1 atm are predicted, indicating that pressure at the electrode/electrolyte interface could contribute to electrode delamination. Additionally, it is found that Darcy's law is insufficient for calculating the pressure distribution without accounting for the flux due to Knudsen diffusion. Additionally, it is found that for the range of properties typical of oxygen electrodes, there is a negligibly small difference between the dusty gas model and binary friction model from a practical standpoint.

08 HYDROGEN↗
DOE OSTI2022

A KMnO 4 -Generated Colloidal Electrolyte for Redox Mediation and Anode Protection in a Li–Air Battery

The rechargeable lithium-oxygen (Li-O 2 ) battery has the highest theoretical specific energy density of any rechargeable batteries and could transform energy storage systems if a practical device could be attained. However, among numerous challenges, which are all interconnected, are polarization due to sluggish kinetics, low cycle life, small capacity, and slow rates. Here, in this study, we report on use of KMnO 4 to generate a colloidal electrolyte made up of MnO 2 nanoparticles. The resulting electrolyte provides a redox mediator for reducing the charge potential and lithium anode protection to increase cycle life. This electrolyte in combination with a stable binary transition metal dichalcogenide alloy, Nb 0.5 Ta 0.5 S 2 , as the cathode enables the operation of a Li-O 2 battery at a current density of 1 mA center dot cm -2 and specific capacity ranging from 1000 to 10000 mA center dot h center dot g -1 (corresponding to 0.1-1 mA center dot h center dot cm -2 ) in a dry air environment with a cycle life of up to 150. This colloidal electrolyte provides a robust approach for advancing Li-air batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

BitGNN: Unlocking the Performance Potential of Binary Graph Neural Networks on GPUs

Graph Neural Networks (GNNs) have shown compelling results in many graph-based learning tasks. They are, however, time-consuming. Recent work has shown a promising direction in improving GNN speed and shrinking the size — network binarization, which binarizes network values and operations. Prior work, however, mainly focused on algorithm designs, leaving it open on how to fully materialize the performance potential. This work fills the gap by proposing techniques to best map binary GNNs and their computations to fit the nature of bit manipulations, optimizations and algorithms to maximize BSpMM kernel efficiency, and solutions to other factors influencing the end-to-end time on GPUs. Results on real-world graphs show that the proposed techniques outperform state of-the-art binary GNN implementations by 21-67× with little accuracy loss.

Chen, Jou-An↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Replicating HCCI-like autoignition behavior: What gasoline surrogate fidelity is needed?

This work seeks to characterize the fidelity needed in a gasoline surrogate with the intent to replicate the complex autoignition behavior exhibited within advanced combustion engines, and specifically Homogeneous Charge Compression Ignition (HCCI). A low-temperature gasoline combustion (LGTC) engine operating in HCCI mode and a rapid compression machine (RCM) are utilized to experimentally quantify fuel reactivity, through autoignition and preliminary heat release characteristics. Fuels considered include a research grade E10 U.S. gasoline (RD5-87), three multi-component surrogates (PACE-1, PACE-8, PACE-20), and a binary surrogate (PRF88.4). Each fuel was studied at lean/HCCI-like conditions covering a wide range of temperatures and pressures that are representative of naturally aspirated to high boost engine operation. Detailed chemical kinetic modeling is also undertaken using a recently updated gasoline surrogate kinetic model to simulate the RCM experiments and to provide chemical insight into surrogate-to-surrogate differences. The LGTC engine experiments demonstrate nearly identical reactivity between PACE-20 and RD5-87 across studied conditions, while faster phasing is seen for both PACE-1 and PACE-8 due to their stronger intermediate- and low-temperature heat release (ITHR/LTHR) at naturally aspirated and boosted conditions, respectively. The RCM experiments reveal typical low-temperature, negative temperature coefficient (NTC) and intermediate-temperature autoignition behaviors at all pressure conditions for RD5-87, which are qualitatively reproduced by all surrogates. Quantitative discrepancies in both autoignition and preliminary heat release are observed for all surrogates, while their ability to replicate RD5-87 autoignition behavior follows the order of PACE-20 > PACE-1 > PACE-8 > PRF88.4. Excellent mapping is obtained between the LGTC engine and the RCM, where the engine pressure-time trajectories can be characterized by the regimes represented by the RCM autoignition isopleths. The kinetic model performs commendably when simulating both autoignition and preliminary heat release of PACE-20, while typically overpredicting ignition delay times for PACE-1, PACE-8 and PRF88.4 at high-pressure and low-temperature/NTC conditions. Sensitivity and rate of production (ROP) analyses highlight surrogate-to-surrogate differences in the governing chemical kinetics where n-pentane initiates rapid OH branching at a faster rate and an earlier timing for PACE-20 than iso-pentane does for PACE-1 and PACE-8, making it computationally more reactive than the other surrogates. The current study highlights the need to include non-standardized properties, such as the lean/HCCI-like autoignition characteristics, in addition to ASTM properties (e.g., RON, MON) as metrics of fuel reactivity and targets to be matched when formulating high-fidelity surrogates that fully capture gasoline advanced combustion behavior such as HCCI-like autoignition.

42 ENGINEERING↗