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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Grazer-induced changes in molecular signatures of cyanobacteria

Algal biomass production is an emerging renewable source of fuels, nutrients, manufacturing materials, and pharmaceuticals. Industrial-scale production is predominantly performed in open raceway ponds that are inexpensive to build, operate, and maintain compared to closed bioreactors. However, these open pond systems suffer from increased opportunities for biological contamination from predators, pathogens, and competitors, which result in reduced biomass quality and yields and often the complete destruction of the crop over a short period of time. Early detection of contaminants is a necessary step of integrated pest management for triggering and informing interventions to prevent crop losses. To develop a sensitive method of detection utilizing mass spectrometry (MS), we used three methods – imaging mass spectrometry (imaging MS), liquid chromatography MS/MS (LC-MS/MS) combined with molecular networking, and gas chromatography MS/MS (GC–MS/MS) – to observe and identify molecular signatures from a model predator-prey system of the heterolobosean amoeba HGG1 preying on the filamentous cyanobacterium Anabaena sp. PCC 7120. Imaging MS enabled the association of molecules with the crop, the predator, or the activity of protozoan grazing, while LC-MS/MS-based molecular networking identified a subset of the grazing-specific signals as the chlorophyll breakdown products pyropheophytin, pheophorbide A, and pyropheophorbide. Application of MS techniques to other amoebacyanobacterial predator-prey pairs allowed categorization of grazing signatures as universal or specific to the species of prey, predator, or the prey-predator pairing, indicating that MS-based techniques can distinguish crops from competitors and potentially identify predators. Finally, GCMS/MS was shown to be capable of monitoring novel volatile organic compounds (VOCs), including those predicted to be released through chlorophyll breakdown, in the headspace over algal cultures under predation. Furthermore, these results demonstrate that the combination of multiple MS technologies creates a predictive framework to identify and catalog relevant molecular signatures for informing crop protection strategies.

09 BIOMASS FUELS↗
DOE OSTI2023

New static solutions of symmetric $\phi^4$ equation

In this paper, we provide new exact solutions of nonlinear Klein–Gordon ( $\phi^4$) equation in 1 + 1-dimension. For simplicity, we focus on the static equation and ignore the time-dependence. The symmetric $\phi^4$ equation has played an important role in several areas of physics. We obtain several novel non-singular solutions of the symmetric $\phi^4$ model in terms of the Jacobi elliptic functions and compare them with the well-known solutions. Finally, we categorize these solutions in terms of the potential parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A comprehensive review of solar only and hybrid solar driven multigeneration systems: Classifications, benefits, design and prospective

Depending on the application and demands, different products can be generated utilizing multigeneration systems. To drive such systems, solar energy can be used as a primary energy source or in hybridization with other renewable or nonrenewable energy sources. Solar driven multigeneration systems are appealing due to the broad availability of solar energy and related technologies. Here, the objective of this study is to review, categorize and discuss different configurations, benefits, potentials, challenges, future research directions and market perspectives of solar energy-driven multigeneration systems, comprehensively. This study also reviews how solar energy systems can be used to hybridize systems for multigeneration purposes. Regardless of using fossil fuel backup sources, these systems are classified as (1) solar only-driven and (2) hybrid solar-driven, in which solar energy is used together with other renewable sources. Reviewing the literature demonstrates numerous ways to build solar energy-driven multigeneration systems by coupling a variety of different cycles and devices. Such systems offer multiple benefits such as enhancing the efficiency, reducing capital and operating costs and carbon dioxide emission. Hybridizing solar with other renewable energies enables continuous operation and dispatchability management. This review shows that for configuration selection and design, different thermodynamics, economic and environmental aspects should be considered. To advance the solar driven multigeneration systems development, further multidisciplinary research on different aspects of such systems is necessary. Particularly, investigations should focus on building different prototypes and on conducting experimental assessments. Also, new incentives and long-term purchase agreements for the products must be established.

14 SOLAR ENERGY↗
DOE OSTI2020

Data-driven evaluation of HVAC operation and savings in commercial buildings

Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air con­ditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗
DOE OSTI2022

A comparison of building system parameters between affordable and market-rate housing in New York City

Low-income households in the United States experience higher than average energy burdens (defined as the proportion of household income spent on energy utilities), and many of these households struggle to simultaneously pay for rent, energy, and basic household necessities. The analysis presented here in this study examines whether the underlying characteristics of buildings and their energy systems could contribute to this disparity for affordable housing residents in New York City. It combines an energy audit dataset of 7,328 multifamily buildings with a database of properties receiving local, state, or federal housing subsidies. The results of this analysis indicate that the building-level installed equipment in large (greater than 50,000 square feet) affordable housing buildings in New York City is more efficient than that in market-rate buildings, but this trend largely disappears when considering overall building characteristics, such as location, size, or age. Significant differences in the types of systems installed in affordable and market-rate housing are also observed, as well as the types of energy efficiency recommendations made by energy auditors. However, these latter data were not normalized by building system characteristics, as that analysis is much more difficult to interpret for categorical data such as heating system type. These findings indicate that retrofit policies and building performance standards focused on affordable housing will likely need to account for underlying differences in building characteristics between affordable and market-rate housing to achieve intended impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2026

A review of energy storage for power system resilience: Functions, metrics, and applications

Aging infrastructure, increasing operational complexity, and surging electricity demand from artificial intelligence and electrification are straining the grid and heightening the risks of disruptions, making resilience a critical priority. Energy storage is increasingly deployed to provide critical power supply, fast grid support, and rapid restoration. However, current practice lacks consistent metrics and systematic methodologies to rigorously quantify the resilience benefits of storage. This paper provides a comprehensive review of energy storage in resilience enhancement, focusing on functional roles, quantification metrics, and integration strategies. A structured resilience metrics library is compiled and categorized to encompass both technical and economic performance aspects. Existing methodologies for resilience-oriented storage planning and operations are critically examined. Key technical and practical challenges are identified, and future research directions are outlined to strengthen storage contributions to grid resilience.

Benefit quantification↗
DOE OSTI2025

Advances in supervisory control strategies for a heat pump centric HVAC system − a comprehensive review on applications

There is an increase in research investigating the development and deployment of supervisory controllers that enable high efficiency electrically driven vapor compression heat pumps operating within grid interactive efficient buildings to provide demand side management. This paper reviews over sixty relevant case studies within this domain that focus on commercial off-the-shelf heat pumps whose primary task is providing space conditioning. The concept of a heat pump-centric heating, ventilation, and air-conditioning system is introduced, accompanied by a detailed overview of various kinds of electric heat pumps and building-level thermal energy storage configurations. Additionally, the different types of supervisory controller designs, including rule-based control and model predictive control, are discussed, along with the various methods for communication between a supervisory controller and a downstream heat pump’s local controller. A comparative analysis is conducted in order to categorize the reviewed case studies based on their system design, supervisory control algorithm, and validation methodology. This detailed analysis allows the review to establish current research trends, identify potential gaps, and suggest future directions for the development of this technology. Overall, the authors recommend that more future research be devoted to low-cost practical retrofits that allow for easy integration of active thermal energy storage within heat pump-centric heating, ventilation, and air-conditioning system systems that utilize direct expansion heat pumps. We also suggest more research into the development and deployment of supervisory controllers that can properly communicate with commercial off-the-shelf heat pump local controller available control inputs, e.g., zone temperature setpoint. Lastly, more rigorous experimental demonstrations of advanced supervisory control within real and or closed-loop, transient/ quasi-steady state environments are necessary to reduce industry wide skepticism of this technology.

Demand side management↗
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 OSTI2023

Ten questions concerning occupant-centric control and operations

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the IEA-EBC Annex 79 research program's subtask on real world implementations of OCC during the past 5 years. First, in Q1 and Q2, we provide a definition and categorization of OCC. Q3 addresses the role of building operators for OCC, while Q4 describes the implications for designers. Then, Q5 and Q6 discuss the role and possibilities of OCC for load flexibility, and for pandemic induced paradigm shifts in the built environment, respectively. In Q7, we provide a taxonomy and selection process of OCC, while Q8 details real world implementation case studies. Finally, Q9 explains the limits of OCC, and Q10 provides a vision for future research opportunities. Furthermore, our findings offer valuable insights for researchers, practitioners, and policy makers, contributing to the ongoing discourse on the future of building operations management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Weather effects on the lifecycle of U.S. Department of Defense equipment replacement (WELDER)

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2024

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%–20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Plastic additives in the ocean: Use of a comprehensive dataset for meta-analysis and method development

In excess of 13,000 chemicals are added to plastics (‘additives’) to improve performance, durability, and production of plastic products. They are categorized into numerous chemical classes including flame retardants, light stabilizers, antioxidants, and plasticizers. While research on plastic additives in the marine environment has increased over the past decade, there is a lack of methodological standardization. To direct future measurement of plastic additives, we compiled a first-of-its-kind dataset of literature assessing plastic additives in marine environments, delineated by sample type (plastic debris, seawater, sediment, biota). Using this dataset, we performed a meta-analysis to summarize the state of the science. Currently, our dataset includes 217 publications published between 1978 and May 2023. The majority of publications analyzed plastic additives in biota collected from Europe and Asia. Analyses concentrated on plasticizers, brominated flame retardants, and bisphenols. Common sample preparation techniques included Solvent - Agitation extraction for plastic, sediment, and biota samples, and Solid Phase Extraction for seawater samples with dichloromethane and solvent mixtures including dichloromethane as the organic extraction solvent. Finally, most analyses were performed utilizing gas chromatography/mass spectrometry. There are a variety of data gaps illuminated by this meta-analysis, most notably the small number of compounds that have been targeted for detection compared to the large number of additives used in plastic production. The provided dataset facilitates future investigation of trends in plastic additive concentration data in the marine environment (allowing for comparison to toxicity thresholds) and acts as a starting point for optimizing and harmonizing plastic additive analytical methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗