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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 865 records · Page 48

All sources NASA NTRS DOE OSTI

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 OSTI2019

Analysis of the Detonation Wave Structure in a Linearized Rotating Detonation Engine

The rotating detonation engine is increasingly favored as a viable pressure gain combustion technology for both propulsion and power generation applications. Practical designs involve the discrete injection of fuel and air, which then partially mix to produce the reactive mixture that is processed by a continuously moving detonation wave within the detonation chamber. One of the fundamental challenges in the successful operation of rotating detonation engines is the design of a robust fuel-oxidizer injection system. To minimize pressure losses while ensuring efficient mixing that promotes stable detonation, the injection has to satisfy many different constraints. In this work, the impact of discrete injection on wave propagation is studied in an effort to understand the impact of fuel stratification on detonation stability. To this end, the structure of the detonation wave within a linearized rotating detonation engine with fully premixed hydrogen-air and hydrogen-oxygen injection at various equivalence ratios is analyzed. The direct numerical simulation of the linearized model detonation engine is compared with experimental results, and wave behavior is correlated to the wave structure and flow turbulence. As a result, the flow properties and chemical composition of the gases across the detonation wave are studied to examine the characteristics of the reaction zone.

42 ENGINEERING↗

DOE OSTI2019

Conversion of Diesel Engines to a Gas and Gas–Diesel Cycle

The conversion of diesel engines to gas and gas–diesel cycles during the production process is analyzed. The requirements for conversion to natural-gas operation are presented. The power, efficiency, and environmental impact of diesel engines, natural-gas engines, and gas–diesel engines are compared.

42 ENGINEERING↗

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 OSTI2013

Training and Mentoring the Next Generation of Scientists and Engineers to Secure Continuity and Successes of the US DOE's Environmental Remediation Efforts - 13387

The DOE Office of Environmental Management (DOE-EM) oversees one of the largest and most technically challenging cleanup programs in the world. The mission of DOE-EM is to complete the safe cleanup of the environmental legacy from five decades of nuclear weapons development and government-sponsored nuclear energy research. Since 1995, Florida International University's Applied Research Center (FIU-ARC) has supported the DOE-EM mission and provided unique research capabilities to address some of these highly technical and difficult challenges. This partnership has allowed FIU-ARC to create a unique infrastructure that is critical for the training and mentoring of science, technology, engineering, and math (STEM) students and has exposed many STEM students to 'hands-on' DOE-EM applied research, supervised by the scientists and engineers at ARC. As a result of this successful partnership between DOE and FIU, DOE requested FIU-ARC to create the DOE-FIU Science and Technology Workforce Development Initiative in 2007. This innovative program was established to create a 'pipeline' of minority STEM students trained and mentored to enter DOE's environmental cleanup workforce. The program was designed to help address DOE's future workforce needs by partnering with academic, government and private companies (DOE contractors) to mentor future minority scientists and engineers in the research, development, and deployment of new technologies and processes addressing DOE's environmental cleanup challenges. Since its inception in 2007, the program has trained and mentored 78 FIU STEM minority students. Although, the program has been in existence for only five years, a total of 75 internships have been conducted at DOE National Laboratories, DOE sites, DOE Headquarters and field offices, and DOE contractors. Over 85 DOE Fellows have participated in the Waste Management Symposia since 2008 with a total of 68 student posters and 7 oral presentations given at WM. The DOE Fellows participation at WM has resulted in three Best Student Poster Awards (WM09, WM10, and WM11) and one Best Professional Poster Award (WM09). DOE Fellows have also presented their research at ANS DD and R and ANS Robotics Topical meetings. Moreover, several of our DOE Fellows have already obtained employment with DOE-EM, other federal agencies, DOE contractors. This paper will discuss how DOE Fellows program is training and mentoring FIU STEM students in Department of Energy's Office of Environmental Management technical challenges and research. This training and mentoring has resulted in the development of well trained and polished young scientists and engineers that will become the future workforce in charge of carrying on DOE-EM's environmental cleanup mission. The paper will showcase FIU's DOE Fellows model and highlight some of the applied research the DOE Fellows have conducted at FIU's Applied Research Center and across the Complex by participating in summer internship assignments. This paper will also present and highlight other Fellowships and internships programs sponsored by National Nuclear Security Agency (NNSA), DOE-EM, NRC, Energy (NE), and other federal agencies targeting workforce development. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Training and certification of human organizational factors designers in nuclear plant engineering - 344

Performing an increasing number of small and large-scope nuclear Human Factors Programs, AREVA NP is facing the challenging situation that (1) well-trained experts in Human Factors or Usability Engineering are currently hard to find and (2) newly hired ones need at least two years 'on the job' to become familiar with the nuclear field and the complexity of engineering Human Factors in this field. To innovatively meet this challenge we focus instead on company-internal engineers. Demonstrated nuclear engineering knowledge and skills and pronounced interest to extend both renders them optimal applicants to the AREVA NP two years Human Organizational Factors Training and Certification program. The AREVA NP training shall convey Human Factors knowledge and skills within the limited time of two years. We also stress the need to change participants' mindset from a technically-biased to a human-centered approach, committed to involve end-users throughout design. Next to psychology principles, user testing, user-centered design, plant operation principles, and the acquisition and documentation of users' requirements, design constraints, and inherent tradeoffs, participants will be trained to apply learned methods in a real environment and meet the challenges of real projects. The following sections provide insights in the training conception and preparation phase, the identification of appropriate candidates, the development of course syllabuses, and the schedule of lectures and tests. The promising experiences made thus far are reported as well. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Increasing ROI Using Your Digital Twin for Model Based Engineering

The purpose of this work is to identify innovative ways of using the plant reference simulator or digital twin differently than it has traditionally been used in the past. This paper will identify reasons that the plant reference simulator will support varying levels of model based design and engineering. I will point out real world examples in which the plant reference simulator was used to identify problems with engineering designs, digital control systems and plant modifications. The earlier these issues can be identified and resolved, the less impact they have on the plant modification or upgrade. By using the plant reference simulator earlier in the engineering process, utilities and engineering organizations can minimize costly outage extensions and increase the return on the investment made in the simulator.

42 ENGINEERING↗

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↗

DOE OSTI2025

Cyber-Informed Engineering (CIE) Guide for States

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Early Fault Detection in Nuclear Systems: A Digital Engineering Approach

Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.

42 - ENGINEERING↗

DOE OSTI2023

Engineering Enzymes for Environmental Sustainability

The development and implementation of more efficient and sustainable technologies is key to delivering our net–zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, PET. Finally, biocatalytic approaches for accessing petroleum–based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Finally, through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of more environmentally sustainable approaches, in order to protect our planet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

L–malic acid production from xylose by engineered Saccharomyces cerevisiae

L-malic acid is widely used in the food, chemical, and pharmaceutical industries. Here, we report on production of malic acid from xylose, the second most abundant sugar in lignocellulosic hydrolysates, by engineered Saccharomyces cerevisiae. To enable malic acid production in a xylose-assimilating S. cerevisiae, we overexpressed PYC1 and PYC2, coding for pyruvate carboxylases, a truncated MDH3 coding for malate dehydrogenase, and SpMAE1, coding for a Schizosaccharomyces pombe malate transporter. Additionally, both the ethanol and glycerol-producing pathways were blocked to enhance malic acid production. The resulting strain produced malic acid from both glucose and xylose, but it produced much higher titers of malic acid from xylose than glucose. Interestingly, the engineered strain had higher malic acid yield from lower concentrations (10 g L –1 ) of xylose, with no ethanol production, than from higher xylose concentrations (20 and 40 g L –1 ). As such, a fed-batch culture maintaining xylose concentrations at low levels was conducted and 61.2 g L –1 of malic acid was produced, with a productivity of 0.32 g L –1 h. Furthermore, these results represent successful engineering of S. cerevisiae for the production of malic acid from xylose, confirming that that xylose offers the efficient production of various biofuels and chemicals by engineered S. cerevisiae.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Building a genome engineering toolbox in nonmodel prokaryotic microbes

The realization of a sustainable bioeconomy requires our ability to understand and engineer complex design principles for the development of platform organisms capable of efficient conversion of cheap and sustainable feedstocks (e.g. sunlight, CO 2 , non-food biomass) to biofuels and bioproducts at sufficient titers and costs. For model microbes such as E. coli , advances in DNA reading and writing technologies are driving adoption of new paradigms for engineering biological systems. Unfortunately, microbes with properties of interest for the utilization of cheap and renewable feedstocks such as photosynthesis, autotrophic growth, and cellulose degradation have very few, if any, genetic tools for metabolic engineering. Therefore, it is important to begin to develop 'design rules' for building a genetic toolbox for novel microbes. Furthermore, we present an overview of our current understanding of these rules for the genetic manipulation of prokaryotic microbes and available genetic tools to expand our ability to genetically engineer non-model systems.

09 BIOMASS FUELS↗

DOE OSTI2019

Engineering Clostridium for improved solvent production: recent progress and perspective

Clostridia are Gram-positive, spore-forming, obligate anaerobic bacteria that can produce solvents such as acetone, ethanol, and butanol, which can be used as biofuels or building-block chemicals. Many successful attempts have been made to improve solvent yield and titer from sugars through metabolic engineering of solventogenic and acidogenic clostridia. More recently, cellulolytic and acetogenic clostridia have also attracted high interests for their ability to utilize low-cost renewable substrates such as cellulose and syngas. Process engineering such as in situ butanol recovery and consolidated bioprocessing (CBP) has been developed for improved solvent titer and productivity. This review focuses on metabolic and process engineering strategies for solvent production from sugars, lignocellulosic biomass, and syngas by various clostridia, including conventional solventogenic Clostridium acetobutylicum , engineered acidogens such as C. tyrobutyricum and C. cellulovorans , and carboxydotrophic acetogens such as C. carboxidivorans and C. ljungdahlii .

09 BIOMASS FUELS↗

DOE OSTI2013

Modeling of reciprocating internal combustion engines for power generation and heat recovery

This paper presents a power generation and heat recovery model for reciprocating internal combustion engines (ICEs). The purpose of the proposed model is to provide realistic estimates of performance/efficiency maps for both electrical power output and useful thermal output for various capacities of engines for use in a preliminary CHP design/simulation process. The proposed model will serve as an alternative to constant engine efficiencies or empirical efficiency curves commonly used in the current literature for simulations of CHP systems. The engine performance/efficiency calculation algorithm has been coded to a publicly distributed FORTRAN Dynamic Link Library (DLL), and a user friendly tool has been developed using Visual Basic programming. Simulation results using the proposed model are validated against manufacturer’s technical data.

combined heat and power, internal combustion engin↗

DOE OSTI2019

Shape memory alloy engine for high efficiency low-temperature gradient thermal to electrical conversion

More than half of the energy generated worldwide is lost as unused thermal energy because of the lack of efficient methodology for harnessing the low-grade heat. In this paper, we demonstrate that shape-memory alloy can be an effective mechanism for recovering low-grade heat. Shape memory alloys exhibit thermally induced martensite to austenite phase transformation and super-elasticity (stress-induced martensitic transformation). Employing these two characteristics, we demonstrate a thermal engine for harnessing waste energy through all modes of heat transfer: convection, conduction, and radiation. In this work, we performed material and heat transfer analysis for achieving high frequency, sustainable and efficient operation of our engine. An optimized shape memory alloy engine generated 36 W per kilogram or 234 kW of electricity per cubic meter of active material. A continuous three-day operation of several SMA engines could generate 7.2 kWh of electricity when installed on a 500 m long hot pipe network. This generated power can potentially reduce the carbon footprint by 5.1 kg of CO 2 illustrating the promise of this technology for addressing climate change.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗