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DOE OSTI2020

Probing Coherent States of Light and Matter in Two-Dimensional Semiconductors. Final Report

The ability to enhance light-matter interactions in engineered optical environments is well-established in micro- and nano-photonics. New classes of materials bring new rich correlations between spin, momentum, and light polarization that can be exploited in these photonic systems for information processing and low-power electronics. Interfacing these novel material features with strong optical interactions suggests the compelling capability to create hybrid light-matter systems harnessing low-energy and protected material properties such as spin. The long-term objective of this program was to investigate the interplay between pseudospin in 2D materials and light, revealing new coherent phenomena. The materials studied are the monolayer transition metal dichalcogenides (TMDs), which support polarization-sensitive optical transitions when isolated to a single sub-nanometer crystal layer in thickness. In particular, monolayer TMD semiconductors such as MoS 2 exhibit degenerate valleys in momentum space with distinct spin character. These valleys can be separately addressed by circularly polarized light, providing a tool for manipulation. Integrating this valley pseudospin of excitons in monolayer TMDs with engineered strong optical excitations to create novel coherent phenomena was a key long-term goal of this research. The scope of this research primarily focused on studying light-matter interactions of valley-sensitive excitations in monolayer TMDs. The scope also included the integration of monolayer materials that support valley-polarized emission into photonic devices. This approach extends prior work on exciton-polaritons in 2D materials toward a platform with far more control and improved light input and output characteristics to aid scalability of these polarization-sensitive phenomena to many-body, long-range optical networks. During the course of the five-year Early Career program, the approaches led to successful progress on each proposed aim. Initial successful work seeded additional research that developed from the main themes, while this program focus evolved toward coherent manipulation of valley polaritons and integration with photonics. Both of these advanced topics set the stage for new capabilities in future innovative research. The results of this project provide advances in materials and methods relevant for new visions of opto-electronics and photonics that can improve information processing in an era of increased focus on quantum and coherent phenomena. The research has also contributed to the training of two postdoctoral scholars and five graduate students.

2D Materials↗

DOE OSTI2022

Enabling Regulatory and Business Models for Broad Microgrid Deployment (White Paper)

This white paper is one of seven being prepared for the Department of Energy (DOE) Microgrid Research & Development (R&D) program as part of a strategy development effort for the next 10 years. The seven white papers focus on the following areas: 1. Program vision, objectives, and R&D targets in 5 and 10 years, 2. T&D co-simulation of microgrid impacts and benefits, 3. Building blocks for microgrids, 4. Microgrids as a building block for the future grid, 5. Advanced microgrid control and protection, 6. Integrated models and tools for microgrid planning, designs, and operations, 7. Enabling regulatory and business models for broad microgrid deployment. This white paper is focused on Topic 7, as a sustainable regulatory and business environment for microgrid development is a foundational element for securing DOE's vision for the future role of microgrids in the U.S. electric sector. The objective of this white paper is to systematically characterize regulatory issues involved in microgrid deployment and microgrid business models, and from this evidence identify a robust and well-justified set of research recommendations for the Department of Energy Office of Electricity, informing programmatic vision, objectives and activities for the DOE Microgrid R&D Program.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Integrating Energy Efficiency Strategies with Industrialized Construction for Our Clean Energy Future: Preprint

NREL’s Industrialized Construction Innovation Team has developed an ambitious plan to accelerate the integration of energy efficiency (EE) strategies with Industrialized Construction (IC). The United States (U.S.) construction industry is beginning to use IC methods to build multifamily apartment buildings to address affordability and labor shortages. Apart from reducing cost of construction and delivery times, the IC method of permanent modular construction has the potential to facilitate the integration of a wide range of EE strategies and advanced controls into such buildings. While there may be unintended EE benefits to IC such as a tighter envelope due to higher construction quality, the process has not been leveraged specifically to enhance EE. NREL aims to claim this missed opportunity and integrate IC benefits with EE as well as advanced controls, distributed energy resources, and grid-friendly design strategies. The paper proposes an ‘IC Assessment Framework’ to achieve affordable zero-energy modular multifamily buildings. Through the selection criteria of Design for Manufacturing and Assembly, the framework aims to distill a broad range of proven EE strategies for site-built into a set of strategies that qualify as easy to integrate for off-site. The output is a Factory Information Model (FIM) that represents a process-based digital twin to enable advanced time-and-motion study, plugs into open source building energy modeling platform (EnergyPlus), and serves as a vital tool facilitating wider adoption of EE integration. Conclusively, the paper delineates next steps for upcoming pilots with NREL’s IC partners towards developing a transformational pathway for our Clean Energy Future.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Virtual Metering for Monitoring Building Energy Consumption

The United States Department of Energy (DOE) has standard metering requirements of commercial buildings for optimizing energy performance. The guiding principles are to continuously track and optimize energy performance and install building-level meters for electricity, natural gas, and steam. Some buildings at Los Alamos National Laboratory (LANL) have physical submeters monitoring their energy consumption, but these meters have proven to be unreliable. And, in most cases, replacing them has proven to be a slow process. Installing new submeters also requires a temporary lockout of the circuit on which they are being installed. Many buildings at LANL contain laboratories with ongoing experiments or data centers, which makes an equipment power outage nearly impossible to plan. This inability to plan power outages results in long-term submeter failures. Although most submeters are eventually replaced, failures lead to missing consumption data for some unpredictable, extended time. A building automation system (BAS) is a system that provides control and monitoring on a building to maintain the operational performance of the building and occupancy comfort. Many buildings at LANL currently have a BAS, and all new renovations and installs will include installing a BAS if one does not already exist. The intended purpose for a BAS is primarily to monitor the health and efficiency of a building; however, it is also possible to calculate equipment power and energy consumption using BAS information. This project aims to use virtual meters to monitor building energy consumption as a cost-effective and minimally labor-intensive alternative to installing physical submeters. The fault detection and diagnostics tool, SkySpark, provides a centralized database for all the data from the various BAS that are active at LANL. This data includes the information that is needed to create virtual meters for heating, ventilating, and air conditioning (HVAC) systems in most buildings, including heating and cooling loads. 9 This report begins with a detailed summary of the project, including the reasoning, procedure, and results. The specific processes of creating the various virtual meters are then identified. Then the limitations are discussed. And, lastly, the results and future potential are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗

DOE OSTI2024

The Digital Engineering Vision for DOME: Facilitating Design, Deployment, and Operations [Poster]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

SinhaRoy_TechPresentation_2024 [Slides]

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗

DOE OSTI2021

OpenDSS-wrapper (Distribution System Co-simulator with Distributed Energy Resource Controls)

Electric grid transformation with the proliferation of distributed energy resources (DER), such as solar photovoltaic (PV), wind, advanced energy storage technologies, and electric vehicles, and the growing use of communication technologies in both transmission and distribution systems are increasing the need to capture the interactions among these systems. Advanced modeling, control, and simulation tools that can perform co-simulation of electric power systems with other domains become indispensable to accurately model these interactions. To address this need, we have developed a codebase that integrates an electric power distribution system simulator, DER models, and DER controls. The codebase is based on OpenDSS, a distribution system simulator, and an existing open source co-simulation framework called HELICS. The contribution and uniqueness of the proposed codebase is that it tailors the generic HELICS framework specifically for distribution grid-related applications. The codebase includes an OpenDSS wrapper that controls the simulation, implements advanced DER controls, and extracts power flows, voltages, and other power system element information from the the distribution network modeled in OpenDSS. Sample HELICS federates, including a federate for OpenDSS, are provided that can communicate messages through the HELICS interface. Sample federates can be modified and additional HELICS federates can be added by the user depending on their use case requirements. SEE ALSO: https://github.com/NREL/dss-cosim

Blonsky, Michael↗

DOE OSTI2021

An Indirect Search for Weakly Interacting Massive Particles in the Sun Using Upward-going Muons in NOvA

I present the first Dark Matter search results using the full data set collected with the upward-going muon trigger in NOvA. Weakly Interactive Massive Particles (WIMPs) are a theoretical non-baryonic form of Dark Matter. The nature of Dark Matter is one of the most exciting open questions in modern physics. Though its existence can be inferred by astrophysical evidence, its properties are not yet understood. If we assume that Dark Matter particles can produce Standard Model particles through their interactions, an indirect search can help shed light on this mystery.The NOvA collaboration has built a 14 kton, fine-grained, low-Z, total absorption tracking calorimeter at an off-axis angle to the NuMI neutrino beam. Even though the detector is optimized to observe electron neutrino appearance from a muon neutrino beam, it has a unique potential for more exotic searches given its excellent granularity and energy resolution and relatively low-energy neutrino thresholds. In fact, with an efficient upward-going muon trigger and sufficient background suppression offline, NOvA is capable of a competitive indirect Dark Matter search for low-mass WIMPs.The idea of the upward-going muon trigger is first to select high-quality muon tracks, then use the timing information of all of the hits of each track to estimate directionality. In this way, the background flux is suppressed by more than a factor of 10^5 at trigger level to a rate of approximately 1 Hz. To further optimize this search, we use only upward-going muons that point to the Sun, so our search occurs at night when the Sun is on the other side of the Earth. This strategy also allows us to use the time when the Sun is above the horizon as a control region to estimate the background. Ultimately, implementation of a cut based and maximum likelihood analysis provides a powerful tool for rejecting background and selecting a sample of neutrino-induced upward-going muons. The overall background rejection power achieved by the analysis is substantial and impressive. Starting with approximately 150,000 events per second, we reduced it to 40 events per year. Since no statistically significant excess was found, a 90\% C.L. upper limit on the expected muon flux of upward-going muons has been set using the upper limit on the number of events given the number of observed events in the signal region. Lastly, by assuming the theory behind the upward-going muon flux, a limit on the WIMP-nucleon spin-dependent cross-section in the Sun was estimated. Although the limits on the spin-dependent cross-section do not appear to be competitive with previous indirect Dark Matter searches, the upward-going muon flux limits are promising. The upward-going muon flux limits could extend these results to a broader class of models that are not specific to the dark matter theory but produce upward-going muons, leading to competitive results.

Principato, Cristiana↗

DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Essay: Quantum Sensing with Atomic, Molecular, and Optical Platforms for Fundamental Physics

Atomic, molecular, and optical (AMO) physics has been at the forefront of the development of quantum science while laying the foundation for modern technology. With the growing capabilities of quantum control of many atoms for engineered many-body states and quantum entanglement, a key question emerges: what critical impact will the second quantum revolution with ubiquitous applications of entanglement bring to bear on fundamental physics? In this Essay, we argue that a compelling long-term vision for fundamental physics and novel applications is to harness the rapid development of quantum information science to define and advance the frontiers of measurement physics, with strong potential for fundamental discoveries. As quantum technologies, such as fault-tolerant quantum computing and entangled quantum sensor networks, become much more advanced than today’s realization, we wonder what doors of basic science can these tools unlock. We anticipate that some of the most intriguing and challenging problems, such as quantum aspects of gravity, fundamental symmetries, or new physics beyond the minimal standard model, will be tackled at the emerging quantum measurement frontier. . Published by the American Physical Society 2024

Physics↗

DOE OSTI2020

Innovation in Radiological Security, Part 2 of 2 – Insights into Developing a Cloud-hosted Security Technology

A Sentry Remote Monitoring System (Sentry-RMS) is a stand-alone security system that provides detection, assessment, and communication of priority alarms as an additional means of thwarting internal and external threats to sites that maintain radiological material. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform is an optional feature of the Sentry-RMS that relays priority alarm information to the identified response stakeholders. Sentry-SECURE is hosted in a cloud environment that abstracts the data owner’s and data consumer’s platforms to allow for greater information sharing. This promotes situational awareness amongst authorized users and enables future innovation among modern response platforms. When securely architecting a cloud solution such as this, the use of design paradigms can be an effective tool to increase the accuracy and reliability of cyber- and information-security-related decisions made throughout the development process. This approach also supports the categorization of design considerations into three levels: industry concepts, project approaches, and data protections for digital processes. Industry concepts consist of the notional underpinnings that guide or motivate a security process, system, or design but often lack any tangible attributes. Project approaches represent decisions made during the design and development process to prioritize a solution, method, or practice above another that may provide a comparable functional output but lacks a desired security benefit. Data protections for digital processes represent the selection, integration, and implementation of specific controls for a given asset. This paper will explore specific examples of how Sentry-SECURE has been designed to account for considerations at each of these three levels, while balancing the operational intent of the platform with the security enhancements necessary to maintain data integrity, availability, and confidentiality.

assessment, RMS, security, physical, cloud, Cyber ↗

DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Frequency-bin photonic quantum information

Discrete frequency modes, or bins, present a blend of opportunities and challenges for photonic quantum information processing. Frequency-bin-encoded photons are readily generated by integrated quantum light sources, naturally high-dimensional, stable in optical fiber, and massively parallelizable in a single spatial mode. Yet quantum operations on frequency-bin states require coherent and controllable multifrequency interference, making them significantly more challenging to manipulate than more traditional spatial degrees of freedom. In this mini-review, we describe recent developments that have transformed these challenges and propelled frequency bins forward. Focusing on sources, manipulation schemes, and detection approaches, we introduce the basics of frequency-bin encoding, summarize the state of the art, and speculate on the field’s next phases. Given the combined progress in integrated photonics, high-fidelity quantum gates, and proof-of-principle demonstrations, frequency-bin quantum information is poised to emerge from the lab and leave its mark on practical quantum information processing—particularly in networking where frequency bins offer unique tools for multiplexing, interconnects, and high-dimensional communications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

A Physical Model Enhanced Data Driven Method for High-Resolution Residential Load Profile Generation

Residential buildings account for significant energy consumption, creating opportunities to offer grid services. As electric utilities seek to implement effective system operation strategies, understanding residential energy consumption patterns becomes essential; However, the time intervals of load profiles measured by utilities' smart meters are typically from 15 minutes to 60 minutes. The low-resolution data make it hard to extract appliance-level load information, which is critical for providing grid services. This paper presents a load profile generator designed to produce synthetic load profiles for residential buildings that emphasizes the importance of accurate representations of realistic energy consumption patterns. The generator takes realistic low-resolution residential load measurements and weather data as inputs, producing 1-minute interval profiles that match the characteristics of the original profiles. Further, this generator can be used to populate load profiles in areas where actual measurements are limited to improve the ability of utilities to analyze their distribution systems. By providing more high-resolution residential building load profiles, this tool supports electric utilities to enhance their residential building load control strategies and improve overall grid stability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Design and Demonstration of a NH3-Fueled Two-Stroke Uniflow Engine for Greenhouse Gas Reduction

The maritime shipping industry is growing increasingly interested in both low and non-carbon-containing fuels to meet future greenhouse gas emission targets. Specifically of interest is ammonia, as it has a relatively high volumetric energy density compared to other future fuels, such as hydrogen, making it more economical to transport. The robust engine architecture of low-speed two-stroke marine engines makes them an ideal candidate for ammonia fuel, overcoming many of the issues surrounding its poor ignitability and low flame speed. If emissions and fueling system challenges can be addressed, retrofits of current low-speed two-stroke dual-fuel engines represent a viable pathway for bringing ammonia engines to market. This study explores these technical hurdles by describing the design, analysis, and experimental validation of a single cylinder research engine converted to operate on ammonia fuel. The engine is a reduced-scale uniflow two-stroke marine engine with two previous hardware configurations available – diesel and high-pressure CNG dual-fuel. A concept study was used to evaluate possible ammonia-fueled engine architectures and the associated tradeoffs and design considerations. With the chosen architecture, low-pressure dual fuel, 1D and 3D analysis tools were used to inform hardware selection and to determine hardware configurations which minimized ammonia-slip. In addition to these considerations the hardware and engine configuration were designed to provide a versatile and robust testing platform. This includes options to test both gaseous and liquid ammonia injection, as well as a wide range of performance parameters such as AFR, swirl, valve timing, SOI, and many others. Design constraints imposed by the existing engine hardware necessitated an iterative loop between design and analysis toolsets, ultimately converging on a final design for the ammonia-conversion hardware. The engine was rebuilt with the new hardware and evaluated in an engine test cell. A new control strategy developed and flashed onto a prototyping electronic control unit allowed for full control over all engine parameters. An initial calibration was developed, providing test data for validation of the engine 1D and 3D models. The impact of the design choices on engine operability and the ability to meet program targets is discussed as well as opportunities for further optimization of the ammonia-conversion hardware, informed by the validated models.

Kaul, Brian [ORNL] (ORCID:0000000184813620)↗