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DOE OSTI2025

Development of Hydrogen Burner for FT4000® Aeroderivative Engine - Final Report

This report details an effort to develop a retrofittable fuel/air injector for the FT4000® aeroderivative gas turbine that enables use of hydrogen as a carbon-free fuel for efficient power generation. The FT4000 engine’s low-NOx combustor was developed by Pratt & Whitney and RTX Technology Research Center with core technology from the Pratt & Whitney PW4000 turbofan aircraft engine. The current FT4000 production engine operates on either natural gas or No. 2 fuel oil with water injection to achieve high thermal efficiency and low emissions. This engine is fielded by Mitsubishi Power Aero and delivers 70 MW of power with a simple-cycle efficiency of over 41% when operating with wet compression. The work reported here advances the technology readiness level of the FT4000 combustor for operation with hydrogen, starting with an experimental assessment of the current production hardware with increasing hydrogen content mixed with natural gas and ending with improved nozzle concepts for fully robust operation with 100% hydrogen. High-pressure single-sector combustor rig tests have been completed, demonstrating the ability for the dual fuel nozzle to operate an FT4000 combustor on 100% hydrogen with low nitrogen oxide (NOx) emissions. Metal temperature measurements and video images of the flame structure from zero to 100% hydrogen highlight opportunities to improve the fuel nozzle robustness for high hydrogen conditions. The design of new fuel/air mixer concepts to improve durability and operability with high hydrogen levels was also completed. A total of eleven new concepts were developed and analyzed, ranging from modifications to the bill-of-materials nozzle to fully clean sheet designs. The concepts were evaluated with non-reacting and reacting flow evaluations to assess the performance of the new hardware designs. Non-reacting tests included Phase Doppler Particle Analysis (PDPA) for droplet size and velocity, mechanical patternation for liquid water flux, and planar laser induced fluorescence (PLIF) with acetone-seeding for gaseous fuel/air mixing characterization. Five scaled candidate fuel nozzle designs, in addition to a scaled bill-of-materials nozzle, were then successfully evaluated in an atmospheric pressure burner rig. The nozzles were evaluated for performance with natural gas, hydrogen/natural gas blends, and pure hydrogen. For 100% hydrogen, the nozzles were evaluated with and without water injection. Optical and infrared imaging of the flame and fuel nozzle was captured. NOx emissions were sampled from fixed emissions probes. The results show measurable differences between the various designs, and the data was used to down-select the two most promising designs to advance to future full pressure rig testing. Results from this study have cleared the current production FT4000 engines with dual fuel nozzles to operate at baseload power on blends of hydrogen mixed with natural gas and water. Two promising nozzle designs have been developed to enable fully robust operation with up to 100% hydrogen. These nozzles require validation at full baseload operating conditions before they can be introduced to the field.

03 NATURAL GAS↗

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 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 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 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 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 OSTI2024

Engineering and evolution of Yarrowia lipolytica for producing lipids from lignocellulosic hydrolysates

Yarrowia lipolytica, an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Verazine biosynthesis from simple sugars in engineered Saccharomyces cerevisiae

Steroidal alkaloids are FDA-approved drugs (e.g., Zytiga) and promising drug candidates/leads (e.g., cyclopamine); yet many of the ≥697 known steroidal alkaloid natural products remain underutilized as drugs because it can be challenging to scale their biosynthesis in their producing organisms. Cyclopamine is a steroidal alkaloid produced by corn lily (Veratrum spp.) plants, and it is an inhibitor of the Hedgehog (Hh) signaling pathway. Therefore, cyclopamine is an important drug candidate/lead to treat human diseases that are associated with dysregulated Hh signaling, such as basal cell carcinoma and acute myeloid leukemia. Cyclopamine and its semi-synthetic derivatives have been studied in (pre)clinical trials as Hh inhibitor-based drugs. However, challenges in scaling the production of cyclopamine have slowed efforts to improve its efficacy and safety profile through (bio)synthetic derivatization, often limiting drug development to synthetic analogs of cyclopamine such as the FDA-approved drugs Odomzo, Daurismo, and Erivedge. If a platform for the scalable and sustainable production of cyclopamine were established, then its (bio)synthetic derivatization, clinical development, and, ultimately, widespread distribution could be accelerated. Ongoing efforts to achieve this goal include the biosynthesis of cyclopamine in Veratrum plant cell culture and the semi-/total chemical synthesis of cyclopamine. Herein, this work advances efforts towards a promising future approach: the biosynthesis of cyclopamine in engineered microorganisms. We completed the heterologous microbial production of verazine (biosynthetic precursor to cyclopamine) from simple sugars (i.e., glucose and galactose) in engineered Saccharomyces cerevisiae (S. cerevisiae) through the inducible upregulation of the native yeast mevalonate and lanosterol biosynthetic pathways, diversion of biosynthetic flux from ergosterol (i.e., native sterol in S. cerevisiae) to cholesterol (i.e., biosynthetic precursor to verazine), and expression of a refactored five-step verazine biosynthetic pathway. The engineered S. cerevisiae strain that produced verazine contains eight heterologous enzymes sourced from seven different species. Importantly, S. cerevisiae-produced verazine was indistinguishable via liquid chromatography-mass spectrometry from both a commercial standard (Veratrum spp. plant-produced) and Nicotiana benthamiana-produced verazine. To the best of our knowledge, this is the first report describing the heterologous production of a steroidal alkaloid in an engineered yeast. Verazine production was ultimately increased through design-build-test-learn cycles to a final titer of 83 ± 3 μg/L (4.1 ± 0.1 μg/g DCW). Finally, this research lays the groundwork for future microbial biosynthesis of cyclopamine, (bio)synthetic derivatives of cyclopamine, and other steroidal alkaloid natural products.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Engineering an Extremely Hybrid PKS for Adipic Acid Production

Polyketide synthases (PKSs) are modular enzymes with exceptional potential as biocatalysts for producing non-native compounds. Here, we report the first PKS-based pathway for adipic acid (AA), an industrial monomer for nylon production, by engineering one of the most extensively hybridized PKS systems to date. Using a retrobiosynthetic approach, we identified EtnB, a succinyl-CoA-loading module that uniquely retains the terminal carboxyl group, enabling access to dicarboxylic polyketide products, rarely produced by canonical PKSs. EtnB was coupled to a fully reducing extension module through an engineered communication linker, which improved ACP–KS interactions, enhanced titers, and demonstrated selective succinyl-CoA loading in vivo . This construct integrates genes from five organisms─with domains from seven PKS modules joined across six non-natural junctions─and functions in both Escherichia coli and Pseudomonas putida . Additional engineering that included AT domain exchanges, optimization of extender unit supply, and host strain metabolic rewiring further increased AA production. Together, this work demonstrates that highly chimeric PKSs can be rendered functional through rational design, expands the PKS toolkit with a carboxyl-retaining loading module, and establishes a versatile platform for engineering diacids and other noncanonical products through PKS pathways.

biomanufacturing↗

DOE OSTI2026

Dressed-State Hamiltonian Engineering in a Strongly Interacting Solid-State Spin Ensemble

In quantum science applications, ranging from many-body physics to quantum metrology, dipolar interactions in spin ensembles are often controlled via Floquet engineering. However, this technique typically reduces the interaction strength between spins and effectively weakens the coupling to a target sensing field, limiting the metrological sensitivity. In this Letter, we develop and demonstrate an alternative method that directly tunes the native dipolar interaction in an ensemble of nitrogen-vacancy (NV) centers in diamond, thereby overcoming these limitations inherent to Floquet engineering. Our approach utilizes dressed-state qubit encoding under a bias magnetic field applied perpendicular to the crystal lattice orientation. This method leads to a 3.2× enhancement of the dimensionless coherence parameter JT 2 compared to state-of-the-art Floquet engineering and a 2.6× (8.3 dB) enhanced sensitivity in ac magnetometry. Furthermore, our results provide a powerful Hamiltonian engineering tool for future studies with NV ensembles and other interacting higher-spin (S > $\frac{1}{2}$) systems.

Quantum control↗

DOE OSTI2024

Alternatives to MARVEL Power Conversion -- Comparison of Stirling Engine Thermal Efficiency and Design to Other Power Conversion Cycles

PRESENTATION for CONFERENCE PAPER: The Microreactor Applications, Research, Validation, and Evaluation (MARVEL) Reactor is a small liquid-metal thermal reactor that will be built at the Idaho National Laboratory to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. Power conversion in the MARVEL design is provided by Stirling engines, which have disadvantages in nuclear environments. Compared to Stirling engine performance, some alternative power cycles can increase power production when coupled to a liquid-metal thermal reactor. In this paper, the thermal efficiency of MARVEL’s power production with Stirling engines is compared to the thermal efficiency of power production with MARVEL and alternative power cycles. Those cycles include a superheated Rankine cycle, open and closed Brayton cycles, and a supercritical carbon dioxide cycle. All cycles (except the Stirling engines) were modeled with an intermediate helium loop to meet MARVEL’s principal design criteria. All models are simple designs with conservative assumptions for consistent comparison. Detailed optimization will depend largely on reactor location and application, and the relative merit of each cycle is discussed for different environmental conditions. The study informs significant early decisions on power cycle design and economics for deployment of advanced microreactors as they move from theory and concept to execution.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Maturing Rational Design Methodologies and Industry Consensus Engineering Standards: Critical Fastened Joints - Solar PV Industry

Critical structural joints can be seen throughout a solar array and are called upon to secure modules and keep racking assembled and able to resist large demands from winds and snow loads. In the relatively new and fast-growing solar PV industry, the important role these hardware assemblies (e.g. clips, clamps, bolts, nuts, washers) play is not well understood by product designers. Failures with critical structural joints are surprisingly common and point to the need for maturing the engineering and assembly of these joints. The wide variety of design concepts (Figure 2&2) demonstrate interesting and innovative ideas but are lacking the basics of fastener engineering seen in matured industries (e.g. transportation, buildings). Complicating the maturing process for critical structural joints is that they are one component in rack supporting structures that exhibits a systems behavior; each component will affect the other and play a key role in maintaining structural integrity. When wind loads the surface of a module, the underlying racking members deflect and twist which in turn imparts forces back into the joints and into the mounted modules. Often, these supporting rack structures exhibit high deflections and low natural frequencies which amplify the demands placed into the joints even in moderate winds. Current engineering practices and associated structural conventions view solar racking support structures as they would a high mass building that exhibit more static behaviors in wind events. Solar structures are unique from high mass buildings and require the development of solar specific industry engineering consensus standards.

14 SOLAR ENERGY↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Alternatives to MARVEL Power Conversion – Comparison of Stirling Engine Thermal Efficiency and Design to other Power Conversion Cycles

The Microreactor Applications, Research, Validation, and Evaluation (MARVEL) Reactor is a small liquid-metal thermal reactor that will be built at the Idaho National Laboratory to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. Power conversion in the MARVEL design is provided by Stirling engines, which have disadvantages in nuclear environments. Compared to Stirling engine performance, some alternative power cycles can increase power production when coupled to a liquid-metal thermal reactor In this paper, the thermal efficiency of MARVEL’s power production with Stirling engines is compared to the thermal efficiency of power production with MARVEL and alternative power cycles. Those cycles include a superheated Rankine cycle, open and closed Brayton cycles, and a supercritical carbon dioxide cycle. All cycles (except the Stirling engines) were modeled with an intermediate helium loop to meet MARVEL’s principal design criteria. All models are simple designs with conservative assumptions for consistent comparison. Detailed optimization will depend largely on reactor location and application, and the relative merit of each cycle is discussed for different environmental conditions. The study informs significant early decisions on power cycle design and economic

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Progress on the MARVEL Cybersecurity by Design Model-Based Systems Engineering Project

Formal model-based systems engineering (MBSE) combines a model, systems thinking, and systems engineering to visually depict the boundaries, context, and behavior of interconnected systems, facilitating effective design, development, and utilization of engineered systems throughout the systems engineering lifecycle. Although nuclear reactor vendors employ these tools to integrate functionality, performance, and safety, they are not yet addressing digital risk concerns introduced by use of operational technology, such as digital instrumentation and control systems. To accomplish this objective, the Microreactor Applications Research Validation and EvaLuation (MARVEL) microreactor was used as an MBSE case study. This real-world application provides a first-of-a-kind opportunity to demonstrate the benefits of integrating digital risk and cybersecurity into the MBSE design process of a nuclear reactor. This paper provides an update of the ongoing MARVEL Cyber MBSE project as it specifically relates to the integration of digital risk management and cybersecurity by design.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Moving from Information Assurance to Functional Assurance with Engineered Controls

Cyber threats to operational technology demand more than traditional IT defenses—they require full-spectrum mission assurance. Cyber-Informed Engineering (CIE) is an approach that embeds engineered controls into system design to ensure critical functions remain safe and reliable, even under attack. Unlike conventional cybersecurity tools, engineered controls act directly on physical processes to prevent unacceptable outcomes such as equipment damage or mission failure. This session will outline the CIE framework and share examples of consequence-based design that deliver true resilience, not just fail-safe behaviors. Attendees will learn how to integrate these principles into the engineering lifecycle to support resilient-by-design architectures and inform emerging standards. This talk sets the stage for the panel discussion on advancing CIE across sectors as digital and physical systems converge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗