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DOE OSTI2022

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

This paper investigates the optimal crankshaft motion for an opposed piston (OP) engine in a novel hybrid architecture to maximize fuel efficiency. The OP engine was selected for this work due to its inherent thermodynamic benefits and the balanced nature of the engine which can achieve downsizing through reducing the number of cylinders rather than the individual cylinder volume. The typical geartrain required on an OP engine was exchanged for two electric motors, reducing friction loss and decoupling the crankshafts. Using the motors to control the crankshaft motion profiles, this architecture introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To leverage these opportunities, an optimization scheme was developed utilizing nonlinear optimization of a 0-D model to compute the crankshaft motion profile that maximizes the work generated by the system. This optimization was then iteratively coupled with a high fidelity model which supplies the cylinder flow boundary conditions. This iterative approach reduces the model complexity used in the optimal control problem (OCP) while capturing the gas exchange dynamics critical to the 2-stroke cycle of the OP engine. By using the rate of change of motor torque as the input to the OCP, the torque fluctuation in a single cycle can be limited to ensure tracking feasibility. The results show crankshaft velocity slows during the compression stroke and conversely accelerates during the expansion stroke, reducing the peak motor torque required for control and thus reducing the motor losses. The extended residence time at top dead center, however, leads to an increase in heat transfer, illustrating the trade-off between the work extraction efficiency and the indicated engine efficiency.

Engineering↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

42 - ENGINEERING↗

DOE OSTI2023

The Neutronic Engine: A Platform for Operando Neutron Diffraction in Internal Combustion Engines

Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates. Here, we present the design of a “neutronic engine,” which is analogous to an optical engine in that the materials and external geometry of a typical automotive engine have been optimized to maximize access of the diagnostic while maintaining the internal combustion chamber geometry and operability of the engine. The high transparency of aluminum to neutrons makes it the ideal window material for neutron diagnostics, which allows the neutronic engine to be a truly all-metal engine with the same load and boundary condition capabilities of a modern downsized passenger car engine. The neutronic engine will enable 3D and time-resolved measurements of strain, stress, and temperature fields as well as phase transformation, texture, and microstructure throughout the metal components of the combustion chamber.

Wissink, Martin↗

DOE OSTI2025

Engineering PHL7 for Improved Poly(Ethylene Terephthalate) Depolymerization via Rational Design and Directed Evolution

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has emerged as a promising approach for polyester recycling, and, to date, many natural and engineered PET hydrolase enzymes have been reported. For industrial use, PET hydrolases must achieve high depolymerization extent and exhibit excellent thermostability. Here, we engineered a natural PET hydrolase, Polyester Hydrolase Leipzig #7 (PHL7), through rational design and directed evolution using a high-throughput screening platform. Four new enzymes were engineered with enhanced properties compared with the parent enzyme, wild-type PHL7 (PHL7-WT), and other benchmark PET hydrolases, under the tested conditions. In bioreactors, the exemplary engineered enzyme, PHL7-Jemez, exhibited improved ability to depolymerize amorphous PET film compared with PHL7-WT at 2.9% and 20% substrate loadings, with 37% and 270% higher hydrolysis, respectively, after 48 h. This study develops several state-of-the-art PET hydrolases and demonstrates a directed evolution platform to engineer high-performance enzymes, which can accelerate enzyme discovery toward improved biocatalytic recycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Data for 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.

Hydrolysate↗

DOE OSTI2025

Cyber-Informed Engineering (CIE) Integration into Model- Based Systems Engineering (MBSE)

Engineering design in the field of industrial engineering, such as designing automated factories or warehouses, is critical for the effective operation of facilities. Any design flaws introduced early can result in significant capital expenses to correct. However, early-stage engineering design is inherently complex. The systems are not yet built, requiring designers to integrate various aspects, including digital engineering and cybersecurity, to support virtual representations throughout the design process. In this study, we propose an approach to integrate Cyber-Informed Engineering (CIE) principles into model-based systems engineering (MBSE). This approach facilitates the development of a digital thread for engineering systems, ensuring secure digital artifacts in the design of industrial engineering systems.

42 - ENGINEERING↗

DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗

DOE OSTI2017

Utilizing the Shift Technical Engineer Pipeline to Develop the Next Generation of Engineers - 17354

As the liquid waste contractor for the U.S. Department of Energy's (DOE) Savannah River Site (SRS), Savannah River Remediation (SRR) is responsible for safely treating and dispositioning approximately 35 million gallons of liquid waste stored at SRS. To achieve this mission, SRR manages and operates several complex nuclear facilities. These nuclear facilities include the H and F Tank Farms, the Defense Waste Processing Facility (DWPF), and the Saltstone Processing and Disposal Facility. To support the operations of these complex nuclear facilities, a Shift Technical Engineer (STE) is stationed for every shift. The STE is the sole Engineering Organization representative on-shift and serves as a direct technical resource for operations, including safety basis interpretation, process monitoring and prompt troubleshooting support. The training and qualification for an STE is more intensive than for typical technical staff personnel at SRR, involving additional classes, under-instruction watches, demonstration of practical factors, and culminating in an intense oral board examination. Like many other contractors in the DOE complex, SRR faces the challenge of preparing a significant number of new generation engineers to take the place of an aging workforce. Beyond the traditional training to establish minimum technical qualifications, SRR has established a number of initiatives aimed at accelerating the process of preparing early career engineers to replace experienced senior engineers. These initiatives include an internal rotational program, technical exchange activities with companies at other DOE sites, and an STE pipeline. The STE role provides a unique mechanism for select, early career engineers to gain in-depth knowledge and significant responsibility normally not achieved until much later in their careers. Once qualification is complete, the engineers perform the STE duties, providing technical support for nuclear facility operations. In addition, creating an STE pipeline, where promising young engineers are continually being qualified to replace the on-shift STEs, provides the company with a source of experienced engineers with in-depth technical knowledge as well as valuable experience interfacing with operations and other facility support groups. This paper will discuss the STE position, the qualification process, and how SRR is using the STE qualification pipeline to benefit both the company and the individual engineer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Remotely detected vehicle mass from engine torque-induced frame twisting

Explore the source record for details and available documents.

McKay, Troy R.↗

DOE OSTI2018

Exploring Engine Oil Reactivity Effects on End Gas Knock in a Direct-Injection Spark Ignition Engine

An experimental study was conducted in a direct-injection (DI) spark-ignited engine to determine the extent to which oil reactivity impacts combustion phasing and knock propensity. Three engine oils were examined: a baseline 20W30 oil from conventional base stock, a 5W30 oil from a synthetic base stock, and a jet oil from a hindered ester base stock. The engine was operated at a constant fueling rate of 24.7 mg/injection for two engine speed conditions (1500 and 2000 rpm) using two cam profile conditions (high and low lift), for a total of four operating conditions. Spark timing sweeps were conducted at each of the four operating conditions. Results were analyzed for an engine oil impact on combustion phasing, cycle-to-cycle variability, combustion duration, knock propensity, and knock intensity. No correlation between engine oil type and any of these performance metrics could be identified. Measurements showed that the oil consumption rate for this engine is low and comparable to engines compliant with U.S. Tier 1 and Tier 2 emissions standards, consuming 1.4 g/kg of fuel consumed (150 g for 20 hours of operation). Finally, the lack of a correlation between the oil type and engine performance can be attributed to this low level of oil consumption, resulting in very little interaction between the oil and the combustion chamber contents.

42 ENGINEERING↗

DOE OSTI2023

Low-carbon fuels for spark-ignited engines: A comparative study of compressed natural gas and liquefied petroleum gas on a CFR engine with exhaust gas recirculation

Decades of work on low-carbon fuels have established their potential for substantial emissions reductions; however, their adoption is still limited by infrastructure concerns and engine efficiency deficits. As infrastructures have begun to evolve, research on strategies that maximize engine efficiency through interactions with fuel properties must also now take center place. This paper compares the performance, emissions, and combustion characteristics of two forefront low-carbon fuels: compressed natural gas (CNG) and liquefied petroleum gas (LPG) in a cooperative fuel research (CFR) engine over a range of compression ratios and engine loads. The effects of exhaust gas recirculation (EGR), end-gas auto-ignition, and a novel combustion control tool, the combustion intensity metric (CIM), were also evaluated at different stoichiometric engine operating conditions. In comparison to LPG, CNG operation demonstrated an extended knock-free regime, allowing engine operation at higher engine loads and compression ratios, but LPG operation exhibited enhanced combustion characteristics with higher peak pressures and faster apparent heat release rates (AHRR). LPG operation achieved higher brake thermal efficiencies and lower equivalent CO 2 emissions compared to CNG operation at the tested engine loads and compression ratios. LPG demonstrated significantly higher EGR tolerance limits compared to CNG, with a maximum of 28% EGR rate, compared to 23% for CNG. This improved EGR dilution tolerance was responsible for a 90% reduction in NOx emissions for LPG compared to a maximum of 70% with CNG. EGR dilution also exhibited more effective knock mitigation potential with LPG, suppressing knock intensity values by up to 98% and transitioning the engine operation towards normal combustion from heavy knocking conditions. As a result, the CIM was found to decrease burn durations and improve the quality of combustion by controlling the desired fraction of end-gas auto-ignition.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence–function relationships across protein engineering campaigns

The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.

Long, Yueming [California Institute of Technology ↗

DOE OSTI2019

Reference Natural Gas Flames at Nominally Autoignitive Engine-Relevant Conditions

Laminar natural gas flames are investigated at engine-relevant thermochemical conditions where the ignition delay time r is short due to very high ambient temperatures and pressures. At these conditions, it is not possible to measure or calculate well-defined values for the laminar flame speed S-l, laminar flame thickness delta(l), and laminar flame time scale tau(l) = delta(l)/S-l due to the explosive thermochemical state. Here, the corresponding reference values, S-R, delta(R), and tau(R) = S-R/S-R, that account for the effects of autoignition, are numerically estimated to investigate the enhancement of flame propagation, and the competition with autoignition that arises under nominally autoignitive conditions (characterised here by the number tau/tau(R)). Large values of tau/tau(R) indicate that autoignition is unimportant, values near or below unity indicate that flame propagation is not possible, and intermediate values indicate that a combination of both flame propagation and autoignition may be important, depending upon factors such as device geometry, turbulence, stratification, et cetera. The reference quantities are presented for a wide range of temperatures, equivalence ratios, pressures, and hydrogen concentrations, which includes conditions relevant to stationary gas turbine reheat burners and boosted spark ignition engines. It is demonstrated that the transition from flame propagation to autoignition is only dependent on residence time, when the results are non-dimensionalised by the reference values. The temporal evolution of the reference values are also reported for a modelled boosted SI engine. It is shown that the nominally autoignitive conditions enhance flame propagation, which may be an ameliorating factor for the onset of engine knock. The calculations are performed using a recently-developed, detailed 177 species mechanism for C0-C3 chemistry that is derived from theoretical chemistry and is suitable for a wide range of thermochemical conditions as it is not tuned or optimised for a particular operating condition. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

autoignition↗

DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗

DOE OSTI2025

Cyber-Informed Engineering (CIE) – Engineered Controls Database and Use

Cyber-Informed Engineering (CIE) addresses the reality that cyber-attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

42 - ENGINEERING↗

DOE OSTI2020

What Questions Would a Systems Engineer Ask to Assess Systems Engineering Models as Credible

Digital Systems Engineering strategies typically call for digital Systems Engineering models to be retained in repositories and certified as an authoritative source of truth (enabling model reuse, qualification, and collaboration). In order for digital Systems Engineering models to be certified as authoritative (credible), they need to be assessed - verified and validated - and with the amount of uncertainty in the model quantified (consider reusing someone else's model without knowing the author). Due to this increasing model complexity, the authors assert that traditional human-based methods for validating, verifying, and uncertainty quantification - such as human-based peer-review sessions - cannot sufficiently establish that a digital Systems Engineering model of a complex system is credible. Digital Systems Engineering models of complex systems can contain millions of nodes and edges. The authors assert that this level of detail is beyond the ability of any group of humans - even working for weeks at a time - to discern and catch every minor model infraction. In contrast, computers are highly effective at discerning infractions with massive amounts of information. The authors suggest that a better approach might be to focus the humans at what model patterns should be assessed and enable the computer to assess the massive details in accordance with those patterns - by running through perhaps 100,000 test loops. In anticipation of future projects to implement and automate the assessment of models at Sandia National Laboratories, a study was initiated to elicit input from a group of 25 Systems Engineering experts. The authors positioning query began with - 'What questions would a Systems Engineer ask to assess Systems Engineering models for credibility?" This report documents the results of that survey.

42 ENGINEERING↗

DOE OSTI2018

Selection Criteria for Sustainable Fuels for High-Efficiency Spark-Ignition Engines with Examination of their Storage Stability, Impact on Engine Knock, and Fine Particle Emissions

It is possible to significantly improve the efficiency of spark-ignition engines given fuels with improved autoignition, evaporative cooling, and particle emission properties. At the same time, a vast range of different fuel chemistries are accessible from biomass - leading to questions about how fuel chemistries outside the range available from petroleum and ethanol can impact engine operation. This presentation will briefly describe the factors leading to poor efficiency in current SI engines, and the technologies available for improving efficiency. Improved fuel properties that enable high efficiency engine designs to be pursued aggressively will be reviewed, including octane index and sensitivity. A screening process based on fuel properties was applied to a large set of proposed biomass-derived gasoline blendstocks, and the properties of the best blendstocks were evaluated. Some of these fuels exhibit poor stability towards oxidation in the liquid phase, and storage stability studies for alkyl furans and cyclopentanone will be presented in brief. The importance of fuel heat of vaporization for direct injection engines, along with new research on measurement of this parameter, will be presented including an SI engine study of the impact of heat of vaporization on octane index and engine knock. Fuel effects on fine particle emissions and how our understanding breaks down for oxygenates will be discussed. Engine combustion experiments, droplet evaporation simulations, and heat of vaporization measurements conducted to better understand how oxygenates affect particle emissions will be described. This research defines a process that can be used to evaluate fuels for other types of combustion such as diesel, gasoline compression ignition, or strategies with mixed modes.

30 DIRECT ENERGY CONVERSION↗