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DOE OSTI2025

Digital Droplet PCR and Mesocosm-Based Methods to Evaluate Biocontainment Strategies in a Native Soil Ecosystem

Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect the complexities found in natural environments, necessitating an environmentally relevant analysis pipeline that allows for the detection of rare escapees within a complex soil microbiome and differentiation between closely related strains. To this end, we have developed an approach that utilizes soil mesocosms and integrated digital droplet PCR (ddPCR) system to evaluate the efficacy of novel biocontainment strategies. We demonstrate the utility of this approach by modeling contamination with industrial microbial chasses versus their biocontained counterparts. Here we demonstrate the broad utility of this system by highlighting findings from strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system, strains of Synechocystis sp. PCC 6803 contained via gene knockout or toxin anti-toxin system, and strains of Escherichia coli that are contained via genomic recoding. We also show that ddPCR can be used to detect gene copies from E. coli equal to those counted by traditional spot plating assays. The resultant data demonstrates that this system has broad utility across diverse microbial chassis and biocontainment strategies and enables researchers to track the fate of our contaminating microbe with high sensitivity in the soil. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Transportation Energy Analytics Dashboard (TEAD)

The investment decisions within the nation’s transportation sector have traditionally been prioritized and measured against safety and travel efficiency goals. Increasingly, federal, state, and local policies are requiring consideration of energy use and emissions in the design of our transportation infrastructure. Yet the processes, analytics, and knowledge to include these new metrics into transportation decisions are lacking. Although targeted studies and before and after analysis are performed from time to time, no comprehensive real-time monitoring systems provide energy and emissions to the same level as congestion and safety. To address this gap, the Center for Advanced Transportation Technology (CATT Lab) and the Maryland Transportation Institute (MTI) at the University of Maryland (UMD), in partnership with the National Renewable Energy Laboratory (NREL), have developed a Transportation Energy Analytics Dashboard, or TEAD, to raise awareness of the energy and emission impacts to the same level of observability as that of safety and mobility concerns. The overall goal of this project was to develop and demonstrate an online tool to monitor transportation energy use and emissions in real-time and to archive this data for retrospective analysis. By combining surface transportation energy and emissions evaluation capability with more traditional safety, mobility and reliability system evaluation, our goal is for TEAD to create a dynamic new paradigm in transportation systems management and performance analyses, for both real-time traffic operations, and longer-term planning for project and program investment decision-making.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Impact of system parameters and geospatial variables on the reliability of residential systems with PV and energy storage

A reliable power supply is the foundation of modern society, enabling technologies used to function within a society. Residential systems are places where the end users directly consume power, enabling technologies to sustain life. With the emergence of behind-the-meter resources, the end-users have some control over power supply reliability. The intermittency and variability of these resources impact residential system reliability. In this work, we study the reliability performance of a grid-supplemented residential system with behind-the-meter Distributed Energy Resources(DER) subject to various system parameters and geospatial variables. We propose a multistate reliability model for the behind-the-meter microinverter-based Photo Voltaic (PV) system and integrated inverter-based energy storage (ES) system. A sequential Monte Carlo method is then presented to evaluate the reliability indices for the residential system with behind-the-meter DERs as the main supply and the grid as the backup. The sequential Monte Carlo method is used to analyze the reliability performance of the residential model at the top 100 populous counties of the United States, where actual load and solar irradiance data at the counties is used. In the analysis, the sensitivity of indices to system parameters such as DER size and the sensitivity of the indices to climate zone and gross horizontal irradiation (GHI), which affects the load conditions and the PV output of the residential system, is performed. The analysis results show that the system parameters and the geospatial variables significantly impact the residential system’s reliability. The insights from this analysis will be of immense value to the distribution system planners to provide zone-specific guidelines for DER system sizing and toward the evolution of utility business models. Finally, the methodology developed can be used to extend the analysis to other locations.

14 SOLAR ENERGY↗

DOE OSTI2020

MN Solar Pathways: Solar Deployment Strategies-Modeling and Case Studies

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Clean Power Research (CPR) developed a Solar Deployment Strategies modeling tool to calculate the economic impacts and market incentives created by different solar portfolios and rate structures. CPR developed the tool using Minnesota-specific data and assumptions. The case studies described in this report provide insight into possible pathways toward the state goal of 10% solar, but the emphasis of the report is the demonstration of the tool in use cases that can be expanded and modified in future years as needs change.

14 SOLAR ENERGY↗

DOE OSTI2023

Temporal performance indicators for an integrated pilot-scale membrane distillation-concentrated solar power/photovoltaic system

Management of concentrate streams in inland applications has uncertain long-term environmental impacts. This study investigates an intensified solar-energy capture desalination system that integrates membrane distillation (MD) with a hybrid concentrated solar power (CSP)/photovoltaic (PV) collector to realize self-sustained zero-waste discharge for effective management of concentrate streams in inland and off-grid applications. The demonstration-scale CSP/PV system can produce up to 178 kWh of thermal energy and 4 kWh of electrical energy per day. The thermal and electrical energy from the CSP/PV system is directly supplied to the air gap MD (AGMD) pilot-scale system producing up to 288 L of distilled water per day. Experiments were performed on the hybrid AGMD-CSP/PV system to evaluate system performance under various operating conditions including AGMD and CSP flow rates, CSP system pre-heating, and AGMD vacuum pressure. Experimental results indicate that doubling the AGMD flow rate results in a 119% increase in thermal energy utilization and a 71% increase in distillate production. Compared to the winter months, operating the system in summer months when direct normal irradiance (DNI) is highest results in nearly double the distillate production (88 L in winter and 168 L in summer) and nearly three times the amount of thermal energy consumption (15 kWh in winter and 43 kWh in summer). Operating with vacuum resulted in a 34% increase in distillate production and allowing the thermal storage reservoir to preheat in the winter resulted in a 61% increase in distillate production. Altogether, experimental results highlight the tradeoff between distillate production and thermal and electrical energy production and consumption under various environmental conditions and the potential for AGMD-CSP/PV to be a stand-alone desalination system.

14 SOLAR ENERGY↗

DOE OSTI2023

Interconnection Cost Analysis in ISO-New England

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data for 194 projects in New England from interconnection studies performed between 2010 and 2021. Project-level cost summary data are available for download on this page. We find: -Interconnection costs have grown over time, especially for projects that withdraw. -Interconnection costs are highest for onshore wind, followed by solar and storage. Natural gas and offshore wind projects tend to cost less to interconnect, in comparison. -Economies of scale exist for solar and possibly storage projects, but not for other resource types. -Wind and solar projects requesting capacity network resource interconnection service have higher interconnection costs, despite being evaluated using the same interconnection standard in the analyzed studies. -Low and high interconnection costs can be found throughout the ISO-NE footprint. -Costs are split fairly evenly between investments at the point of interconnection and within the broader network for active and withdrawn projects, while complete projects incur most costs at the point of interconnection.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

NFE-24-10417: Durability and Life-time Energy Performance Enhancement of Vapor Compression Systems

This project investigated the use of a Centrifugal Particle Separation (CPS) system to improve the durability and operational reliability of outdoor heat exchangers in vapor-compression based heating, ventilation, air conditioning, and refrigeration (HVACR) systems. The developed method introduced a novel dust and fouling mitigation approach by integrating an ambient air pre-cleaner that employs centrifugal forces to remove particulate matter from incoming air before it reaches the heat exchanger. By reducing the accumulation of dust and debris on coil surfaces, the CPS system aims to lower maintenance requirements and extend equipment lifespan. The project specifically involves coupling a centrifugal air pre-cleaner with a single-zone ductless mini-split system and evaluating its effectiveness in maintaining cleaner coil surfaces. Additionally, the study examined the impact of CPS integration on system energy performance. Through controlled experimental testing and performance monitoring, this work seeks to validate CPS-based air pre-cleaning as a practical, efficient solution for mitigating outdoor coil dust accumulation and fouling in both residential and commercial HVACR applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating Short-warning Mitigation via Intentional Robust Disruption of a Hypothetical Impact of Asteroid 2023 NT1

We investigate various short-warning mitigation scenarios via fragmentation for a hypothetical impact of asteroid 2023 NT1, a near-Earth object (NEO) that was discovered on 2023 July 15, two days after its closest approach to Earth on July 13. The asteroid passed by Earth within ∼0.25 lunar distances, with a closest approach of ∼1 × 10 5 km and a velocity of 11.27 km s −1 . Its size remains largely uncertain, with an estimated diameter range of 26–58 m and a most probable estimate of 34 m (JPL Sentry, 2023 September 15; weighted by the NEO size frequency distribution). If 2023 NT1 had collided with Earth, it could have caused significant local damage. Assuming a spherical asteroid with a diameter of 34 m, uniform density of 2.6 g cm −3 , and impact velocity of 15.59 km s −1 , a collision would have yielded an estimated impact energy of ∼1.5 Mt, approximately 3 times the energy of the Chelyabinsk airburst in 2013. We analyze the effectiveness of mitigation via intentional robust disruption for objects similar to 2023 NT1. We utilize Pulverize It (PI), a NASA Innovative Advanced Concepts study of planetary defense via fragmentation, to model potential mitigation scenarios through simulations of hypervelocity asteroid disruption and atmospheric ground effects in the case of a terminal defense mode. Simulations suggest that PI is an effective multimodal approach for planetary defense that can operate in extremely short interdiction modes, in addition to long interdiction timescales with extended warning. Our simulations support the proposition that threats like 2023 NT1 can be effectively mitigated with intercepts of 1 day (or less) prior to impact, yielding minimal to no ground damage.

Asteroids↗

DOE OSTI2024

Advanced multimaterial shape optimization methods as applied to advanced manufacturing of wind turbine generators

Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.

17 WIND ENERGY↗

DOE OSTI2023

Techno-economic analysis of the water, energy, and greenhouse gas emissions impacts from the adoption of water efficiency practices in the U.S. manufacturing sector

Water is a critical resource for all of manufacturing. As such, mitigating risks to the supply of water to manufacturing plants facilitates maintaining a vital manufacturing sector. Reductions in water use through water efficiency is one method for mitigating risk, however little is known about its potential in the United States (U.S.) manufacturing sector. Herein, we address this gap and calculate the water reduction potential in U.S. manufacturing from commercially available water efficiency opportunities specific to the manufacturing sector. A list of water efficiency measures specific to the manufacturing sector is compiled and applied to estimates of U.S. manufacturing water withdrawals. The energy and carbon dioxide equivalent (CO 2 e) emission impacts from implementation of these measures are also quantified to better inform the resource, environmental, and economic trade-offs from implementation of these measures. We find that the water savings potential from the evaluated measures is up to 60 percent of U.S. manufacturing water withdrawal for certain subsectors, with the energy and emissions impact dependent on the measure. Further, we calculate the levelized cost of conserved water for several water efficiency measures (-$\$4,000$ to $\$85$/thousand m 3 , where negative values indicate net cost savings and positive values indicate net cost expenditures) and compare them to the levelized cost of alternative water sources ($\$600$ to $\$3,400$/thousand m 3 ). The results show significant opportunities for water and energy use reductions at levelized costs at least one order of magnitude lower than alternative water supplies, with some being revenue-generating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience

U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.

Brigantic, Robert T.↗

DOE OSTI2020

Numerical study of advanced compression ignition and combustion in a gasoline direct injection engine

Multi-mode combustion strategies may provide a considerable thermal efficiency improvement targeted at part-load operating conditions for light-duty spark-ignition (SI) engines. The extension from boosted SI mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing compression ratio and utilizing intake air heating. In order to enable an accurate control of intake charge condition for ACI control and rapid mode-switches, it is essential to gain fundamental insight into the autoignition process with regard to thermal and fuel-air mixture stratification in the combustion chamber. In this work, a computational fluid dynamics (CFD) study is carried out to reveal some degrees of correlation between the mixture and thermal stratifications induced by the cylinder wall temperature and combustion-phasing across varying engine load conditions. The computational analysis begins with a calibrated simulation setup best matching the engine experiments, and subsequently evaluates the baseline setup for an extended range of engine load conditions for two excess-air ratio cases. The present study emphasizes the dominance of thermal stratifications for autoignition process due to wall temperature effects depending on the engine load conditions of interest. In addition, this study also aims to provide insight into the impact of mixture stratification on cyclic variability, especially at colder wall engine conditions (e.g., cold start). Observations herein provide highlight of the propensity of autoignition in correlation with mixture reactivity space.

Advanced Compression Ignition (ACI) engine↗

DOE OSTI2024

Integrated assessment modeling of a zero-emissions global transportation sector

Currently responsible for over one fifth of carbon emissions worldwide, the transportation sector will need to undergo a substantial technological transition to ensure compatibility with global climate goals. Few studies have modeled strategies to achieve zero emissions across all transportation modes, including aviation and shipping, alongside an integrated analysis of feedbacks on other sectors and environmental systems. Here, we use a global integrated assessment model to evaluate deep decarbonization scenarios for the transportation sector consistent with maintaining end-of-century warming below 1.5 °C, considering varied timelines for fossil fuel phase-out and implementation of advanced alternative technologies. We highlight the leading low carbon technologies for each transportation mode, finding that electrification contributes most to decarbonization across the sector. Biofuels and hydrogen are particularly important for aviation and shipping. Our most ambitious scenario eliminates transportation emissions by mid-century, contributing substantially to achieving climate targets but requiring rapid technological shifts with integrated impacts on fuel demands and availability and upstream energy transitions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A multi‐variable framework for selecting WRF physics configurations at convection‐permitting scales: An Amazon wet‐season case study

Tropical convection over rainforests modulates atmospheric circulation and the energy and hydrological cycles across multiple scales. However, the scarcity of observations still limits our understanding of these processes. Although numerical models are utilized to investigate atmospheric physical processes, their performance depends on the choice of parameterizations and grid resolution. Here, in this work, we introduce and apply a multi‐variable, multi‐physics framework to evaluate and rank the Advanced Research Weather Research and Forecasting (WRF–ARW) configurations at 1‐km resolution over the central Amazon during the wet season. A 48‐member ensemble combines three land‐surface models (LSMs), four planetary boundary layer (PBL), and four microphysics (MP) schemes. Model performance for seven convection‐related near‐surface and boundary‐layer variables is assessed using Taylor diagrams and the Taylor skill score (TSS), analysis of variance (ANOVA)‐based sensitivity metrics, and non‐parametric rank tests. We then construct a combined, weighted TSS to identify configurations that are comparatively robust across variables. Results showed that most configurations reproduce near‐surface temperature, sensible and latent heat fluxes, and boundary‐layer height reasonably well, whereas humidity and rainfall remain challenging. LSM choice has the strongest impact on the surface fluxes and a secondary influence on near‐surface temperature and humidity, PBL schemes dominate boundary‐layer height, and MP schemes exert the largest control on rainfall. No single configuration is optimal for all variables, but the combination of Noah (LSM), Yonsei University (PBL), and Morrison (MP) emerges as the most robust configuration for this case, with the WRF single‐moment six‐class scheme (WSM6) providing a competitive, computationally cheaper MP alternative. The framework is general and can be applied to other regions, seasons, and convective regimes.

Amazon rainforest↗

DOE OSTI2024

Development of prechamber enabled mixing-controlled combustion strategy for ultra-low methane emissions from lean burn natural gas engines

This numerical study explores the optimization of Prechamber Enabled Mixing-Controlled Combustion (PC-MCC) using natural gas in heavy-duty engines, aiming to enhance combustion efficiency to minimize methane slip and NOx emissions. The approach involves a prechamber ignition system, distinct from conventional spark ignition (SI) systems, to initiate combustion of direct injected natural gas. By leveraging the robust ignition characteristics of the prechamber, the PC-MCC method demonstrates significant potential in achieving efficient combustion akin to diesel engines but with lower greenhouse gas emissions. The research evaluates the effects of various geometric and operational parameters on the combustion process and emissions, including prechamber volume, nozzle diameter, direct injector (DI) geometry, and engine operating strategies. Computational Fluid Dynamics (CFD) simulations are utilized, focusing on a heavy-duty, single-cylinder engine modeled after the Caterpillar C9.3B engine. Key findings indicate that a prechamber volume of 3 cc, coupled with a nozzle diameter of 2.75 mm for two prechamber holes, strikes an optimal balance between combustion efficiency and emissions reduction. This configuration ensures robust combustion across a range of operating conditions while maintaining methane slip within targeted limits. Further investigation into DI geometry shows the significance of the injector umbrella angle and nozzle diameter in shaping the fuel-air mixing and combustion dynamics. An umbrella angle of 130° and a nozzle diameter of 300 microns are identified as optimal, promoting rapid and efficient combustion with minimized methane and NOx emissions. The study also investigates the impact of injection timing and pressure, highlighting their roles in controlling combustion timing and influencing emissions levels. Advanced injection timing is found to be crucial in achieving the desired low methane slip, whereas retarded injection timing assists to reduce NOx emissions while having a slight increase in methane emissions. Operating strategies incorporating various levels of Exhaust Gas Recirculation (EGR) are assessed for their effectiveness in further reducing emissions. The research demonstrates that a judicious combination of internal hot EGR and careful calibration of DI pressure and SOI timing can achieve significant reductions in NOx emissions while keeping methane slip under control. Specifically, an internal EGR level of 15%–25%, combined with DI pressures of 200–300 bar and injection timings at or after top dead center, is recommended. These findings contribute valuable insights into the development of advanced combustion techniques for natural gas engines, offering a viable pathway to reduce methane slip without compromising engine efficiency or performance. The PC-MCC system presents a promising solution for the future of heavy-duty natural gas engine technology to reduce methane emissions.

Nsaif, Osama↗

DOE OSTI2023

Reliable, High Power Density Inverters for Heavy Equipment Applications

With this final report, the combined team of the University of Arkansas (UA), University of Illinois, Urbana-Champaign (UIUC), Wolfspeed, Caterpillar, and Ampaire have successfully met all of their project objectives. Noteworthy for the heavy equipment portion of the project with Caterpillar is that the team made its project milestones two years into the project by designing a power dense motor drive for a permanent magnet synchronous machine. Upon finding out that Caterpillar had pivoted to switched-reluctance machines (SRMs), the team subsequently redesigned and implemented the SRM drive with a coolant temperature of 105°C! The other major task that the UA, UIUC, and Wolfspeed teams took on was the design of a PMSM drive for a hybrid aircraft that was flown on Feb. 20, 2023 by Ampaire after extensive testing and evaluation. While there were also technical objectives in thermal management, integrated gate drivers, reliability studies, and high temperature capacitors with integrated bussing, each of these have been fully reported on in quarterly reports. In brief, advances in thermal management and high temperature capacitors were utilized in order to achieve a 105°C motor drive. The integrated gate driver work resulted in a higher density drive with no loss of efficiency. Most of the last year, during a no-cost extension, was spent waiting for the Ampaire motor drive to be tested (outside of our project). Many months passed with the device just sitting in California while the company dealt with battery-related issues. This delayed the integration and testing activities until Fall 2022. Once those began, then the process took about 4-5 months to complete culminating in the test flight in Feb. 2023. By providing technical advances and integration into final platforms, the barrier to economic impact has been lowered. This project benefits the public by overcoming key technical barriers to electrified and hybrid electric heavy equipment and aircraft. This, in turn, leads to lower greenhouse gas emissions and a cleaner environment. This final report summarizes the integrated gate driver work and the Ampaire hybrid electric aircraft integration and test flight efforts. All other information has been previously reported in quarterly reports. A summary of the motor drives created during this project is provided along with a listing of publications.

42 ENGINEERING↗