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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes

Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems. Automation makes these systems more vulnerable and increases the consequences of cyberattacks. This study utilizes an infrastructure-based communication platform consisting of cooperative adaptive cruise control and lane control advisories developed by the authors to perform cyber risk assessment of CAVs. The study emulates three types of cyberattacks (message falsification, dedicated denial of service, and spoofing attacks) in a representative traffic environment consisting of multiple CAV platoons and lane change events to analyze the safety and stability impacts of the cyberattacks. Simulation experiments using VISSIM reveals that traffic stream and CAV string is unstable under all three types of cyberattacks. The worst case is represented by the message falsification attack. Increases in volatility are observed over a no attack case, with variations increasing by an average of 43%–51% along with an increase of over 3000 crash conflicts. Similarly, lane change crash conflicts are observed to be more severe compared to rear end crash conflicts, showing a higher probability of severe injuries. Further, the case of slight cyberattack on a single CAV also creates significant disruption in the traffic stream. Analysis of variance (ANOVA) reveals the statistical significance of the results. Furthermore, these results pave the way for future design of secure systems from a monitoring perspective.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗
DOE OSTI2020

Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint

With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.

building energy data↗
DOE OSTI2023

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. Furthermore, the impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2025

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗
DOE OSTI2023

"PoliMOR: A Policy Engine \"Made-to-Order\" for Automated and Scalable Data Management in Lustre"

Modern supercomputing systems are increasingly reliant on hierarchical, multi-tiered file and storage system architectures due to cost-performance-capacity trade-offs. Within such multi-tiered systems, data management services are required to maintain healthy utilization, performance, and capacity levels. We present PoliMOR, a pragmatic and reliable policy-driven data management framework. PoliMOR is composed of modular, single-purpose agents that gather file system metadata and enforce policies on storage systems. PoliMOR facilitates automated and scalable data management with customizable agents tailored to HPC facility-specific storage systems and policies. Our evaluations demonstrate the scalability and performance of PoliMOR both by its individual agents and as a collective entity. We believe PoliMOR is widely applicable across HPC facilities with large-scale data management challenges and will garner interest from the HPC community, given its flexible and open-source nature.

George, Anjus↗
DOE OSTI2022

Interactive Exploration of High-Dimensional Phase Diagrams

High-dimensional thermodynamic phase stability databases are becoming increasingly common due to the convergence of three recent trends: (i) the widespread interest in so-called “high-entropy” alloys, (ii) the availability of high-throughput computational assessments of phase stability in broad composition spaces and (iii) the ongoing development of ever-increasingly broad, multicomponent, multiphase CALPHAD databases. Although automated computational tools can readily process such high-dimensional data, scientists are often unable to visualize the relevant phase relations, an ability that is crucial to gaining an intuitive understanding of the stability constraints governing materials design. The present work addresses this need by providing algorithms that enable the interactive exploration of phase equilibria in high-dimensional spaces. These algorithms concentrate the complex nonlinear nonsmooth optimization needed into a preprocessing step that generates a large number of high-dimensional yet elementary graphical primitives. Furthermore, these primitives can then be cross-sectioned to yield 3-dimensional views in a computationally efficient manner that enables an interactive exploration of high-dimensional spaces. All of these operations are highly parallelizable, thus facilitating scaling of this method to large data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TranSyT , an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE’s release, software development for the automatic integration of transport reactions into models has stalled. Here, in this paper, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data Visualization: Augmented Reality

In recent years, there has been an increasing interest in developing new technologies for automated characterization and visualization of condition monitoring data. Augmented Reality (AR) is a technology that is being developed to improve such data visualization. Augmented reality has been defined as a technology that merges virtual and physical components in real-time, and in three dimensions. Wearable, commercially-available AR devices allow onsite engineers and technicians to perform inspection tasks with significantly more available information such as comparisons of past and present sensor and imager data, onsite data analysis and result displays, and various forms of metadata including technical drawings, previous inspection reports and maintenance histories, operation manuals, codes and standards, and holograms representing data analysis results superimposed onto the in situ monitored system.

42 ENGINEERING↗
DOE OSTI2023

Analyzing Insider Risk Threat to the Internet of Things (IoT)

Recent technological advancement has created a growing convergence of innovation. From machine learning to ubiquitous computing to wireless networks and automation, the world is seeing new technology increasingly capable of connecting with each other. Devices and systems use open communications networks to interact, process information, and react. This is called the Internet of Things (IoT) and is comprised of physical devices that exchange data over networks, creating revolutionary possibilities. The most common way most people interact with an IoT is through ‘smart home’ products like Amazon’s Alexa, which use microphones, speakers, and phones to control a variety of devices, from lights and thermostats, to cameras, to appliances and vacuum cleaners. But the open nature of IoT networks—necessary for their ability to communicate and operate—also introduces privacy and security concerns. At a personal level, this might mean a hack into a home to steal private information, but when applied in broader industries like healthcare, transportation, manufacturing, or the military, this vulnerability can have serious consequences. As IoT usage and interconnectivity increases, so too does the susceptibility to malicious actors. And the entire system is only as secure as its least secure member. This creates particular risk and vulnerability to radiological material industries, as a competent insider adversary could utilize the IoT to potentially steal or access classified or sensitive information about employees, sites, or systems; or simply sabotage security or maintenance from a more remote—and less secure—device. The IoT relies on a secure network across the entire system, especially in transport which may lack the security of more permanent locations; if one device fails, it can create a ripple effect and an insider threat may seek to exploit that connectivity. While IoT benefits drive increased innovation and usage, there are also vulnerabilities an insider threat could exploit; this risk of an IoT to radiological material must be addressed in any mitigation effort.

Kinney, Justin↗
DOE OSTI2024

Legal and Regulatory Aspects Related to Unmanned Aerial Systems (UAS) for Safeguards Implementation

The International Atomic Energy Agency (IAEA) is exploring the use of innovative technologies such as Unmanned Aerial Systems (UAS) to address safeguards verification challenges such as difficulties in accessing nuclear material in high radiation environments, and remotely located installations, as well as to increase the efficiency of safeguards implementation by automating repetitive and time-consuming tasks. As UAS continue to advance and their use becomes more frequent in high security conscious industries such as nuclear, their advantages in contributing to greater safety and security may weigh in favor of their use. However, as the IAEA begins to use these technologies, the legal, policy, and operational considerations associated with these advanced technologies will also need to be considered to identify any key opportunities, challenges, or areas of concern for the IAEA and its Member States. This paper will explore these areas and identify important considerations for the IAEA and its Member States to consider as the use of UAS is explored further for safeguards applications. It will also provide a series of recommendations for anticipating and addressing legal, regulatory, and policy challenges related to the use of UAS for safeguards implementation.

Siserman-Gray, Ioana-Cristina↗
DOE OSTI2023

Supporting Nuclear Energy Research with MLOps

Artificial intelligence (AI) and machine learning (ML) have been shown to be increasingly helpful tools in a growing number of use-cases relevant to scientific research, despite significant software-related obstacles. There exist large technical costs to setting up, using, and maintaining AI/ML models in production. This often prevents researchers from utilizing these models in their work. The growing field of machine learning operations (MLOps) aims to automate much of the AI/ML life cycle while increasing access to these models. This paper presents our initial work in creating a nuclear energy MLOps platform for use by researchers at Idaho National Laboratory (INL) and aims to reduce the barriers of using AI/ML in scientific research. We hope to promote the integration of the latest AI/ML technologies into researchers' workflows and create more opportunity for scientific innovation. In this paper we discuss how our MLOps efforts aim to increase usage and the impact of AI/ML models created by engineers. We also present several use-cases that are currently integrated. Finally, we evaluate the maturity of our project as well as our plans for future functionality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida

Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the titers, rates and yields needed for commercial viability. Here, we present a molecule- and host-agnostic active learning process for media optimization that is enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70% increases in titer, and 350% increase in process yield in three different campaigns for flaviolin production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed that, surprisingly, common salt (NaCl) is the most important component influencing production. The optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that performance improvements for active learning are rarely monotonous. This work illustrates how machine learning and automation can change the paradigm of current synthetic biology research to make it more effective and informative, and suggests a cost-effective and underexploited strategy to facilitate the high titers, rates and yields essential for commercial viability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

ABSTRACT Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCE Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: is qubit tune-up able to be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗
DOE OSTI2024

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn t enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗