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DOE OSTI2025

Energy Supply Security Index (eesi) v0.1.0

This software computes different metrics that quantify energy supply security. It computes metrics for the amount of energy self-production, autonomy, system adequacy, self-sufficiency and autarky. The software has been developed to compute metrics that are introduced in a journal manuscript, and the software is intended to be published to make the underlying metric calculation accessible and reproducible to the reader of the article. Potential use of the software is for policymakers, energy planners and researchers to guide energy system infrastructure decisions.

Sulzer, Matthias [Swiss Federal Laboratories for M↗

DOE OSTI2024

Promoting Sustainable Transportation Modes: A Systematic Review of Behavior-Change Strategies

In previous studies, many travel-behavior-change strategies often relied on single behavior determinants or psychological theories, overlooking the incorporation of sociopsychological theories for guidance in their design. Integrating these theories could offer consistent guidance for program developers and enhance intervention effectiveness. This paper systematically reviews interventions targeting travel-behavior change, with a focus on self-determination theory and its principles of satisfying individuals’ competence, autonomy, and relatedness needs for enacting change. Additionally, experiment design methods, including randomized controlled trials and quasi-experimental designs, are reviewed and discussed. Key findings highlight the effectiveness of personalized interventions and integrating feedback with goal-setting strategies. Given the limited direct references to sociopsychological theories in existing studies, we explore relevant sociopsychological theories applicable to travel-behavior-change programs to provide examples of how strategies could be designed based on them. This review contributes valuable insights into the development of strategies for changing travel behavior, offering a theoretical framework for researchers and practitioners to guide intervention design, experimentation, and evaluation. In conclusion, leveraging these theories not only facilitates reproducibility but also provides a standardized approach for transportation demand management program developers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗

DOE OSTI2021

Problematizing “Planning Ahead”: A Cross-Cultural Analysis of Vietnamese Health and Community Workers’ Perspectives on Advance Care Directives

Informed by values of autonomy and self-determination, advance care planning assumes that individuals should independently take control of their future health. In this article, we draw on research conducted with Vietnamese health and community workers to problematize individualized approaches to planning ahead, reframe notions of “cultural and linguistic barriers,” and expose how homogeneous messages about care at the end of life are not readily translatable within and across diverse groups. Anthropological and feminist critiques of inclusion and exclusion are used to reorientate Anglophone framings of the individual and of cultural and linguistic differences. In this article, we suggest that it is the narrow singularity of care for the self—rather than diverse relationalities of care—that should be overcome if aging and end-of-life care policy and practice is to be broadened and made relevant to migrant and non-English-speaking groups.

Nguyen, Nga↗

DOE OSTI2024

Practical low-temperature gasoline combustion for very high efficiency off-road, medium- and heavy-duty engines

Low-temperature gasoline combustion (LTGC) with additive-mixing fuel injection (AMFI) is a new combustion strategy that has been demonstrated to deliver 9%–25% better brake thermal efficiency than similar-sized market-leading diesel engines over the operating map. Moreover, the LTGC-AMFI engine shows near-zero smoke, and NOx emissions are 4–100 times lower than those of a diesel, sufficiently low that no aftertreatment, or only passive NOx aftertreatment, would be sufficient (diesel exhaust fluid is not required). LTGC-AMFI combustion is based on kinetically controlled compression ignition of a dilute charge with a variable amount of low-to-moderate fuel stratification. Fast combustion control is provided by adding minute amounts of an ignition-enhancing additive into the fuel each engine cycle to control its reactivity. This strategy was used to operate a medium-duty (MD) LTGC-AMFI engine at loads from idle to 16.3 bar BMEP and speeds from 600 to 2400 rpm with regular E10 gasoline, which covers nearly the entire operating map of a typical MD engine. Turbine-out temperatures were sufficient for an oxidation catalyst to control hydrocarbon and CO emissions. Autonomie simulations over the GEM ARB Transient and the GEM 55 mph Cruise driving cycles for class-6 trucks using this technology showed fuel economies of 8.1 and 11.4 mpg-gasoline-equivalent, respectively, corresponding to 18.6% and 13.4% improvements over a similar-size diesel engine. Engine-out NOx emissions were 0.024 and 0.01 g/bhp-h, respectively, well below current U.S. emission standards. These results show that switching from diesel to LTGC-AMFI engines would greatly reduce greenhouse gas (GHG) emissions for off-road, MD and HD applications, which will continue to rely on combustion engines because electrification is not practical in the foreseeable future. Finally, with their reduced fuel consumption, the lower cost of gasoline compared to diesel fuel, and much lower aftertreatment costs, LTGC-AMFI engines also offer a significantly lower total cost of ownership.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Assessing Self-Definition and Relatedness in Level of Personality Functioning

The two polarities model (TPM) of personality organizes psychological assessment and psychotherapy and connects to personality disorder diagnosis using the DSM-5 Alternative Model for Personality Disorders (AMPD). The authors developed scales assessing the TPM from an existing self-report measure for level of personality functioning (LPF), a core component of the AMPD. Iterative content analyses of the LPF measure yielded scales for Autonomy and Communion corresponding to dimensions of the TPM. The scales were refined via internal consistency analyses using a measure of psychological attachment and studied in development and validation samples. Associations with relevant external criteria were explored in a series of multiple regressions. Furthermore, the new content-based LPF scales were illustrated with a case vignette. Although the new Autonomy/Communion scales await further validation prior to clinical use, initial evidence suggests that they may bridge the nomological nets of the TPM and AMPD and potentially offer clinical utility in assessment and treatment planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

TEAMER: Electrically Engaged Undulation System for Unmanned Underwater Vehicles

This TEAMER RFTS 1 (Request for Technical Support) project supported the flume tank testing of a long range, high endurance unmanned underwater vehicle (UUV) to monitor maritime space. Today, battery-powered remotely operated vehicles (ROVs) lack the duration to make persistent, wide-area data collection possible.The proposed solution, an Electrically Engaged UnduLation (EEL) drone, can sustain missions for longer duration through hydrodynamic energy harvesting. Power is provisioned via the piezoelectric effect, a material-led phenomenon that converts applied stress into electricity. The EEL subsystems include power, propulsion, navigation, ballast, telemetry, and instrumentation. By mimicking the gait of aquatic eels, EEL can counter currents during maneuvering and level-flight. The identified opportunity is in the future capability of extreme endurance UUVs in swarms. The specific goal for the EEL development is to expand the spatio-temporal coverage of the existing ocean observation mission by overcoming significant challenges of autonomous robotics. Some of the challenges presented include novel compliant mechanism for robust actuation, bio-inspired design to emulate efficient locomotion, smart material-based energy harvesting for sustained power, and swarming architecture through enabled autonomy.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging - Software and Data

Software and testing data from the OH Hinsdale Wave lab for DOE-funded project on Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging. This project will perform foundational research and testing to accelerate the sector-wide development and deployment of marine energy converters to provide Power-At-Sea. Specifically, we seek to overcome known challenges and knowledge gaps for the successful co-design of coupled Wave Energy Converter (WEC)-Autonomous Underwater Vehicles (AUV) systems; systems designed and tested for WEC array system health and environmental monitoring applications. This project brings together an experienced, multi-institution, and multi-disciplinary team to focus on the co-design of marine energy (ME) technologies and AUV docking systems, including multi-body hydrodynamic modeling, active control, autonomy, and hardware interfaces necessary to enable new WEC-focused understanding, and allow for robust and ubiquitous AUV docking and recharging in real-world conditions.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Detailed Simulation Datasets Quantifying U.S. DOE VTO/HFTO R&D Benefits Across Light- to Heavy-Duty Vehicles

For more than 20 years, Argonne National Laboratory’s Vehicle & Mobility Systems Department has assessed how R&D investments by the U.S. Department of Energy’s Transportation Technologies Office and Alternative Fuels and Feedstocks Office affect vehicle energy use and cost. The analyses are performed using Autonomie, Argonne’s full-vehicle simulation tool for energy consumption, performance, and cost. The study covers five time frames ranging from present day through 2050, with more than 30 vehicle classes and applications (10 light duty and >20 medium and heavy duty), as well as six powertrain configurations (conventional, start-stop, hybrid electric vehicle, plug-in hybrid electric vehicle, battery-electric vehicle, and fuel cell electric vehicle) and five fuels (gasoline, diesel, natural gas, hydrogen, and electricity). Low and high technology uncertainty scenarios have been considered to capture a realistic range of outcomes. The resulting datasets include the assumptions used (i.e., efficiency, $/kWh), vehicle-level data (power, energy, weight, and cost), and outputs such as energy consumption, manufacturer’s suggested retail price, and total cost of ownership. These data are critical to stakeholders working in transportation, technology assessment, and long-term R&D planning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

SNL HPC 2018 [Slides]

A new arms race is emerging among global powers: the hypersonic weapon. Hypersonics are flight vehicles that travel at Mach 5 (five times the speed of sound) or faster. They can cruise in the atmosphere, unlike traditional exo-atmospheric ballistic missiles, allowing stealth and maneuverability during midflight. Faster, lower, and stealthier means the missiles can better evade adversary defense systems. The U.S. has experimented with hypersonics for years, but current investments by Russia and China into their own offensive hypersonic systems may render U.S. missile defense systems ineffective. For the U.S. to avoid obsolescence in this strategically significant technology arena, hypersonics—combined with autonomy—needs to be a force multiplier.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2018

Gasoline Engine and Fuels Offering Reduced fuel Consumption and Emissions (GEFORCE)

The GEFORCE project resulted from a collaborative proposal submitted to a U.S. DOE funding opportunity announcement. The CRADA reported herein was put in place to support the GEFORCE project. The project objective was to establish key engine technologies and fuel characteristics that enable very high fuel efficiency with very low emissions in future vehicles. A matrix of study fuels was designed and produced to support engine experiments in an advanced engine. The advanced engine was constructed and prepared for operation in an engine test cell at a 3rd party laboratory. ORNL conducted engine experiments using a 2.0 LTG engine and used the resulting data to estimatefuel economy values for a mid-size sedan using a vehicle model established in Autonomie. The projected fuel economy trends versus driving schedule were consistent with those identified in a previous projectconducted in partnership with the Coordinating Research Council (CRC), AVFL-20. Additional vehicle modeling is anticipated when data are available from the advanced engine.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Comparison of Distributive Consensus Algorithms for AnomalyDetection on the Power-Grid

As an increasing number of devices in the power-grid rely on high-precision clocks to measure electrical properties and time stamp data, GPS receivers have become a vulnerability on the power-grid. GPS spoofing attacks, in which a fake signal is transmitted, can cause sensor measurement errors that can misinform protective equipment and grid operators for both the transmission and distribution grid. The technology being developed to secure precision clocks in the transmission grid are not appropriate for the distribution grid. Thus, there is a need to developed anomaly detection algorithms that are suitable for the hardware available on the distribution grid. Traditionally, a distributed detection framework is comprised of spatially distributed nodes which acquire observations about a quantity of interest and sends the data to the fusion center, where a global decision is made. Instead the distribution grid has low-power devices, such as smart inverters and microPMUs, that could be used to perform highly parallelized calculations. Having on node calculations is beneficial since it is more network and computation efficient, as the more calculations can be performed where the data is being captured instead of communicating across a slow network to a centralized fusion center. Instead, we could employ an alternate peer-to-peer type of information exchange on the distribution grid that can still reach a global decision. A decentralized approach to anomaly detection makes efficient use of the computational and network bandwidth that exists, and it also lacks the single point of failure of a centralized approach. Thus, a decentralized local information exchange and/or inference is preferred. Lastly, the unreliability of the distribution grid network connections and potential noise or error in the measurements must be considered. In order to produce an effective detection tool, a Byzantine-fault tolerant and asynchronous algorithm must be developed. Thus, the first phase of our project is to develop a distributed, decentralized, asynchronous, Byzantine-fault tolerant, collaborative autonomy-based anomaly detection algorithm to collectively analyze clusters of data for signs of a malicious attack.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Direct Injection 4.3L Propane Engine Research Development and Testing (Final Technical Report)

The objective of this project is to develop a propane-fueled direct injection (DI) engine and emissions control system based on a current production General Motors 4.3 L spark-ignited, DI V6 engine. The team will integrate this engine and aftertreatment system into a package delivery vehicle, and demonstrate a 20% greenhouse gas (GHG) improvement over the baseline gasoline 6.0 L engine version of the vehicle while meeting U.S. Environmental Protection Agency Phase 2 medium-duty/heavy-duty and GHG emission regulations. Engine dynamometer, chassis dynamometer, and in-use fleet test data will be incorporated into the required Autonomie simulations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

BrainSLAM

This research aims to develop brain-inspired solutions for reliable and adaptive autonomous navigation in systems that have limited internal and external sensors and may not have access to reliable GPS information. The algorithms investigated and developed by this project was performed in the context of Sandas A4H (autonomy for hypersonics) mission campaign. These algorithms were additionally explored with respect to their suitability for implementation on emerging neuromorphic computing hardware technology. This project is premised on the hypothesis that brain-inspired SLAM (simultaneous localization and mapping) algorithms may provide an energy-efficient, context-flexible approach to robust sensor-based, real-time navigation.

42 ENGINEERING↗

DOE OSTI2019

Explaining jurisdictional compliance with California’s top-down streamlined solar permitting law (AB 2188)

In the U.S., the process by which residential rooftop solar photovoltaic (PV) system installations are permitted and inspected by an “authority having jurisdiction” (AHJ) varies across AHJs. As the U.S. has roughly 23,000 AHJs with varying degrees of local autonomy, improving the cross-jurisdictional consistency of permitting and inspection is not a straightforward task. One promising approach is a “top-down” streamlined solar permitting (SSP) mandate by U.S. States to their AHJs. This study set out to understand the factors associated with the likelihood that an AHJ either did or did not comply with California’s top-down streamlined solar permitting law, AB2188, and then to consider how these factors might be relevant to the future prospects of top-down SSP outside of California. This report provides background material on AB2188 and tests five hypotheses about the factors that affect the likelihood that an AHJ will comply or not comply with AB2188, as developed from the policy diffusion literature and background knowledge on AB2188 and its implementation.

14 SOLAR ENERGY↗

DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗

DOE OSTI2020

Integrated Military Systems

The Integrated Military Systems (IMS) Center at Sandia National Laboratories is a technical systems integrator of high-risk, high-impact systems. It provides cutting-edge technologies, subsystems, solutions, and products that provide a competitive advantage at every point of engagement. Building on more than six decades of experience, IMS seeks to pursue and provide solutions to the most challenging national security issues. We leverage technical capabilities from the Laboratories' core mission and innovatively apply technology to defense problems. IMS' diverse capabilities produce revolutionary technologies in the areas such as precision strike and defense, autonomy, NG&C, directed energy, signal processing/ATR, lethality and threat assessment, and systems engineering. Our talented engineers and staff take a problem from initial design all the way through testing and fielding.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2019

Understanding Adoption of a Key Soft Cost Reduction Strategy: Modeling Administrative Choices Regarding Streamlined Solar Permitting (Final Technical Report)

In the U.S., the process by which residential rooftop solar photovoltaic (PV) system installations are permitted and inspected by an “authority having jurisdiction” (AHJ) and interconnected to the grid varies across the country. As the U.S. has roughly 23,000 AHJs and 3,000 utilities with varying degrees of local autonomy, permitting, inspection, and interconnection (PI&I) heterogeneity represents a significant “soft cost” barrier to the growth of the residential solar PV market. Burkhardt et al. (2015), for example, finds that variations in local permitting procedures alone can lead to differences in average residential PV prices of approximately ${$}$0.14-${$}$0.18/W. When the authors considered jurisdictional variation in permitting plus regulatory procedures, that price differential grew to ${$}$0.64-${$}$0.93/W between the least-favorable and most-favorable jurisdictions. The objective of this project was to determine suites of PI&I reforms that fit the preferences of AHJs and utilities nationwide, as mediated through the preferences of PV installers, with the hoped-for impact of significantly improving the consistency of PI&I for solar PV across the U.S. To accomplish this objective, the project team set out to design and implement survey instruments with embedded discrete choice experiments (DCEs) that used individual PI&I reforms as attributes, with levels informed by a detailed understanding of the PI&I reforms. The analysis of the surveys was to include both defined and latent class analysis of the survey results, in order to identify different sets of AHJs and utilities that would prefer different “mass customized” SSP practice combinations. Note that this project would also have advanced the social science by applying well-established choice analysis techniques to the novel setting of public administration.

14 SOLAR ENERGY↗