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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Hybrid electric vehicle and method of operating engine of the same

A hybrid electric vehicle (HEV) for multiple operation modes includes: a gasoline diffusion flame (GDF) combustion engine configured to perform gasoline diffusion flame combustion; a motor-generator operatively connected to the GDF combustion engine and configured to selectively drive the HEV with electric power of a battery or generate electric power to charge the battery; and a multi-mode controller including a processor and configured to receive operating conditions of the GDF combustion engine and the motor-generator and define a plurality of mode operating regions based on the received operating conditions. In particular, the plurality of mode operating regions includes: an electric vehicle (EV) only mode operating region, a GDF mode operating region where the GDF combustion engine operates and drives the HEV while the motor-generator stops, and a GDF+EV mode operating region where the motor-generator assists the operation of the GDF combustion engine to drive the HEV.

Bourcier, Mark↗

DOE OSTI2023

Methods and systems for coordinating predictive cruise control, engine-off coasting, and hybrid power split

The enclosed disclosure relates to hybrid vehicles and systems with an engine, a drivetrain with a clutch and a transmission, an electric machine, and a controller. The controller receives lookahead information within a lookahead window and present state information of the hybrid vehicle. The controller determines a predicted coasting opportunity exceeding a predetermined threshold within the lookahead window and determines a cruise control reference speed, a power split between the engine and the electric machine, and a timing of enabling engine-off coasting during the coasting opportunity. The controller deactivates the engine and disengages the clutch at a start of the coasting opportunity when the engine-off coasting is enabled.

Gupta, Rohinish↗

DOE OSTI2024

Systems and methods for thermal barrier coatings to modify engine component thermal characteristics

An engine component comprises one or more thermal barrier coatings applied to one or more areas of the engine component. The thermal barrier coatings reduce the temperature rate of change of the areas to which the thermal barrier coating is applied. Reducing the temperature rate of change can help reduce lattice structure damage caused by different temperatures in different areas of the engine component. The thermal barrier coating can be applied as a monolithic layer on a surface of the engine component or can be applied in different areas using patterns. The patterns allow for the tuning of the performance characteristics (temperature rate of change) of the areas of the engine component and can help reduce defect propagation, such as cracks, in the thermal barrier coating from one area of the thermal barrier coating to other areas of the thermal barrier coating.

Koci, Chad Palmer↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Cloning and characterization of a panel of mitochondrial targeting sequences for compartmentalization engineering in Saccharomyces cerevisiae

Mitochondrion is generally considered as the most promising subcellular organelle for compartmentalization engineering. Much progress has been made in reconstituting whole metabolic pathways in the mitochondria of yeast to harness the precursor pools (i.e., pyruvate and acetyl-CoA), bypass competing pathways, and minimize transportation limitations. However, only a few mitochondrial targeting sequences (MTSs) have been characterized (i.e., MTS of COX4), limiting the application of compartmentalization engineering for multigene biosynthetic pathways in the mitochondria of yeast. In the present study, based on the mitochondrial proteome, a total of 20 MTSs were cloned and the efficiency of these MTSs in targeting heterologous proteins, including the Escherichia coli FabI and enhanced green fluorescence protein (EGFP) into the mitochondria was evaluated by growth complementation and confocal microscopy. After systematic characterization, six of the well!performed MTSs were chosen for the colocalization of complete biosynthetic pathways into the mitochondria. As proof of concept, the full alpha-santalene biosynthetic pathway consisting of 10 expression cassettes capable of converting acety coA to alpha-santalene was compartmentalized into the mitochondria, leading to a 3.7 fold improvement in the production of alpha-santalene. Furthermore, the newly characterized MTSs should contribute to the expanded metabolic engineering and synthetic biology toolbox for yeast mitochondrial compartmentalization engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Modeling of thermal and kinetic processes in non-equilibrium plasma ignition applied to a lean combustion engine

In recent years novel ignition systems have been developed to enable stable and efficient engine operations with lean mixtures. Among them, radio-frequency corona ignition systems create discharges that involve a much wider region compared to traditional spark, and produce non equilibrium plasma with high levels of active radicals and excited species. These devices considerably increase the early flame growth speed and extend stable operating limits. With the aim of expanding the knowledge on high efficiency lean-burn SI engines, this paper investigates and compares the combustion development generated by spark and corona ignitions through computational fluid dynamics, within the Reynolds-Averaged Navier-Stokes framework for turbulence modeling. In order to simultaneously take thermal and chemical effects into account, the Perfectly Stirred Reactor combustion model is used. Experimental data are also collected for validation in an optical access engine, for different mixture levels, from stoichiometric to very lean. Furthermore, the faster burn rate generated by the corona system in the initial stage of the combustion is well predicted by the simulations, in all the relative air-fuel ratio conditions. Remarkably, as the mixture becomes lean, simulations are able to capture the non-linear transition from fast to slow kernel growth, before a self-sustainable flame propagation is established. This correlates very well with the measured engine cyclic variability and the corresponding steep change in the duration of the flame kernel formation. Ultimately, this study highlights the important role of the atomic oxygen, as active radical, in promoting and enhancing the combustion initiated by a corona discharge, in addition to the volumetric ignition effect. By contrast, the validated simulations allow to explain that the high-temperature thermal plasma generated in a traditional spark discharge is insensitive to kinetic aspects.

42 ENGINEERING↗

DOE OSTI2025

Multigene engineering in plants: Technologies, applications, and future prospects

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AI-aided plant engineering↗

DOE OSTI2022

Engineering Interlayer Electron–Phonon Coupling in WS 2 /BN Heterostructures

In van der Waals (vdW) heterostructures, the interlayer electron–phonon coupling (EPC) provides one unique channel to nonlocally engineer these elementary particles. However, limited by the stringent occurrence conditions, the efficient engineering of interlayer EPC remains elusive. Here we report a multitier engineering of interlayer EPC in WS2/boron nitride (BN) heterostructures, including isotope enrichments of BN substrates, temperature, and high-pressure tuning. The hyperfine isotope dependence of Raman intensities was unambiguously revealed. In combination with theoretical calculations, we anticipate that WS2/BN supercells could induce Brillouin-zone-folded phonons that contribute to the interlayer coupling, leading to a complex nature of broad Raman peaks. We further demonstrate the significance of a previously unexplored parameter, the interlayer spacing. By varying the temperature and high pressure, we effectively manipulated the strengths of EPC with on/off capabilities, indicating critical thresholds of the layer–layer spacing for activating and strengthening interlayer EPC. Our findings provide new opportunities to engineer vdW heterostructures with controlled interlayer coupling.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗

DOE OSTI2022

Optogenetics Illuminates Applications in Microbial Engineering

Optogenetics has been used in a variety of microbial engineering applications, such as chemical and protein production, studies of cell physiology, and engineered microbe–host interactions. These diverse applications benefit from the precise spatiotemporal control that light affords, as well as its tunability, reversibility, and orthogonality. This combination of unique capabilities has enabled a surge of studies in recent years investigating complex biological systems with completely new approaches. We briefly describe the optogenetic tools that have been developed for microbial engineering, emphasizing the scientific advancements that they have enabled. In particular, we focus on the unique benefits and applications of implementing optogenetic control, from bacterial therapeutics to cybergenetics. Lastly, we discuss future research directions, with special attention given to the development of orthogonal multichromatic controls. With an abundance of advantages offered by optogenetics, the future is bright in microbial engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Enhancing the efficiency benefit of thermal barrier coatings for homogeneous charge compression ignition engines through application of a low-k oxide

Prior experiments reported by the authors have proven the hypothesis that achieving a dynamic temperature swing on the combustion chamber surface will lead to improved thermal and combustion efficiencies of the homogeneous charge compression ignition engine. A thin layer of yttria-stabilized zirconia, roughly 150 μm, was plasma sprayed on the piston top. It led to markedly advanced ignition and heat release in the gasoline homogeneous charge compression ignition engine, accompanied with reduced unburnt hydrocarbon and carbon monoxide emissions, improved combustion efficiency, and a higher thermal efficiency. A related computational study highlighted the critical role of coating thermal conductivity in achieving a desired dynamic response; hence, the second phase of experimental investigations focused on introducing structured porosity in the yttria-stabilized zirconia coating, as a means of reducing effective conductivity. Indeed, additional incremental improvements were observed, as well as limitations related to adverse effects of the surface roughness and the fuel interactions with the surface roughness and open pores. Erosion can also be a problem in a direct injection engine. Therefore, the third round of investigations focused on a material with a natively low conductivity (low-k), sprayed on the top of an Al piston in a relatively dense form, and in a way that yields a smooth surface. The objective was to capitalize on the low conductivity, while avoiding the pitfalls accompanying high-porosity formulations. The heat-storage capacity was limited by keeping the thickness relatively low. The results verify the paramount importance of thermal conductivity in the context of high “temperature swing” behavior and indicate a potential to improve the homogeneous charge compression ignition engine’s combustion efficiency roughly 1.5%, with the overall indicated efficiency improvement on the order of 5%, on a relative basis. In addition, the low-k oxide thermal barrier applied to the piston extended significantly the low-load homogeneous charge compression ignition operability limit.

42 ENGINEERING↗

DOE OSTI2020

Laminar-to-turbulent flame transition and cycle-to-cycle variations in large eddy simulation of spark-ignition engines

This paper investigates the effect of laminar-to-turbulent flame transition modeling on the prediction of cycle-to-cycle variations (CCVs) in large eddy simulation (LES) of spark-ignition (SI) engines. A laminar-to-turbulent flame transition model that describes the non-equilibrium sub-filter flame speed evolution during an early stage of flame kernel growth is developed. In the present model, the flame transition is characterized by the flame kernel size at which the flame transition ends, defined here as the flame transition scale. The proposed model captures the effects that variations in a turbulent flow field have on the evolution of early-stage burning rates, through variations in the flame transition scale. The proposed flame transition model is combined with the front propagation formulation (FPF) method and a spark-ignition model to predict CCVs in a gasoline direct injection SI engine. It is found that multi-cycle LES with the proposed flame transition model reproduces experimentally-observed CCVs satisfactorily. When the transition model is not considered or when variations in the transition process are neglected, CCVs are significantly under-predicted for the case considered here. These results indicate the importance of modeling the laminar-to-turbulent flame transition and the effect of turbulence on the transition process, when predicting CCVs, under certain engine conditions. The LES results are also used to analyze sources for variations in the flame transition. It is found, for the present engine case, that the most important source is the cycle-to-cycle variation in the turbulence dissipation rate, which is used to measure the strength of turbulence in the proposed model, near a spark plug. The large-scale velocity field and the variations of the laminar flame speed due to the mixture composition and thermal stratification are also found to be important factors to contribute to the variations in the flame transition.

42 ENGINEERING↗

DOE OSTI2023

Sustainability Engineering: Challenges, Technologies, and Applications

Sustainability Engineering: Challenges, Technologies, and Applications focuses on emerging topics within sustainability science and engineering, including the circular economy, advanced recycling technologies, decarbonization, renewable energy, and waste valorization. Readers will learn the trends driving today's sustainability research and innovation as well as the latest in sustainable process technologies. This book: addresses emerging sustainability development challenges, progress, and disruptive technologies; discusses biological sustainability, recycling technologies, and sustainable process design and manufacture; and features a comprehensive view from renowned experts who are leaders in their respective research areas. This work is aimed at an interdisciplinary audience of engineers and scientists working on solutions to advance the development and application of sustainable technologies, including - but not limited to - chemical and environmental engineers.

advanced recycling technologies↗

DOE OSTI2023

Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization

Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.

09 BIOMASS FUELS↗

DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

From Atom to Engine: Understanding Fundamental Effects of Structure on Combustion Using Tandem Experiment and Computation

The ability to tie structural features to a fuel candidate’s ignition properties provides a path for rational design of advantaged fuels that facilitate higher-efficiency, lower-emitting combustion in engines. The time evolution of a fuel’s radical population directly correlates with global combustion parameters, and because the generated radicals depend on the initial structure and reaction pathways of the fuel molecule, it follows that the molecular structure of a fuel has a direct impact on this cascade, and therefore salient ignition-tied fuel properties. This work aims to understand the impact of structural features in the context of global combustion properties for direct application to emerging engine technologies. Specifically, we highlight the ability to identify potential fuel blendstocks for specific engine strategies by examining and elucidating the radical cascade tied to ignition. A representative subset of alcohols possessing a variety of structural features (e.g. branching, chain length) was selected for study using a robust combination of experimental and computational methods. The approach begins with identifying short-lived radicals and likely abstraction sites upon pyrolysis in a microreactor coupled with photoionization mass spectrometry for direct detection of product species. In tandem, electronic structure calculations were performed to calculate the relevant potential energy surfaces for fuel pyrolysis as well as the subsequent O2 addition-isomerization reactions, which govern ignition behavior. Furthermore, the theoretical energetic and ro-vibrational data are used to calculate rate constants for direct ignition modeling to explore how structural effects impact NTC behavior. The microreactor data combined with electronic structure theory calculations provide a well-informed picture of the reactions relevant to ignition for each fuel in this study, as well as the most likely radicals generated under engine conditions. This thorough fundamental picture of fuel decomposition and ignition chemistry is used to correlate the observed ignition behavior to molecular structural effects.

advanced biofuels↗

DOE OSTI2025

Cyber-Informed Engineering (CIE) Guide for States

The Cyber-Informed Engineering (CIE) Guide for States provides state energy offices, public utility commissions, and partner organizations with a structured framework for integrating cyber-resilient engineering practices into energy planning, grantmaking, interconnection processes, and workforce development. As grid digitalization and the adoption of distributed energy resources accelerate, states face expanding cyber-physical risks that traditional cybersecurity measures alone cannot fully address. CIE offers a proactive, consequence-focused engineering methodology that emphasizes eliminating or mitigating high-impact failure modes through design, physical controls, and operational safeguards. The guide outlines the 12 core CIE principles, demonstrates their application through state-focused use cases—including grant evaluation rubrics, interconnection reviews, allow-list development, and training programs—and provides practical tools such as scoring frameworks, impact assessment methods, and implementation checklists. It also highlights pathways for state–utility collaboration and opportunities for technical assistance from national laboratories. By adopting CIE, states can enhance grid reliability, reduce lifecycle costs, strengthen supply-chain assurance, and foster a security-aware engineering culture that aligns with broader resilience and modernization goals. November 2025

29 ENERGY PLANNING, POLICY, AND ECONOMY↗