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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 OSTI2024

Life Cycle Inventory Availability: Status and Prospects for Leveraging New Technologies

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

blockchain platforms↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Dive into Underwater Solar Cells

Our oceans are vast, mostly unexplored and difficult to monitor. Large-scale implementation of a fully autonomous 'Internet of Underwater Things' would transform how we collect and share data from this domain; however, deployment is prohibited by the lack of persistent power sources. In principle, underwater solar-energy generation can complement the use of batteries and provide a solution, although dedicated research is needed since traditional silicon solar cells do not perform well underwater due to water's strong absorption of near-infrared light. In this Perspective we present examples of solar-powered underwater applications and discuss which types of solar-harvesting materials could be appropriate, including GaInP variants, CdTe, organic semiconductors, and perovskite semiconductors. We also discuss challenges that need to be addressed, such as the development of effective antifouling coatings and new certification standards given that underwater conditions are starkly different from those in terrestrial environments.

antifouling coatings↗
DOE OSTI2025

Certified randomness using a trapped-ion quantum processor

Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers, realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.

computer science↗
DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short-Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Thermomagnetic generators for ultra-low-grade marine thermal energy harvesting

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s -1 ). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m -1 K -2 with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. Further, the films also show excellent flexibility with 92% retention of the power factor (PF) after 10 3 bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm -2 at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Over 31% efficient indoor organic photovoltaics enabled by simultaneously reduced trap-assisted recombination and non-radiative recombination voltage loss

Indoor organic photovoltaics (OPVs) have shown great potential application in driving low-energy-consumption electronics for the Internet of Things. There is still great room for further improving the power conversion efficiency (PCE) of indoor OPVs, considering that the desired morphology of the active layer to reduce trap-assisted recombination and voltage losses and thus simultaneously enhance the fill factor (FF) and open-circuit voltage for efficient indoor OPVs remains obscure. Herein, by optimizing the bulk and interface morphology via a layer-by-layer (LBL) processing strategy, low leakage current and low non-radiative recombination loss can be synergistically achieved in PM6:Y6-O based devices. Detailed characterizations reveal the stronger crystallinity, purer domains and ideal interfacial contacts in the LBL devices compared to their bulk-heterojunction (BHJ) counterparts. The optimized morphology yields a reduced voltage loss and an impressive FF of 81.5%, and thus contributes to a high PCE of 31.2% under a 1000 lux light-emitting diode (LED) illumination in the LBL devices, which is the best reported efficiency for indoor OPVs. Additionally, this LBL strategy exhibits great universality in promoting the performance of indoor OPVs, as exemplified by three other non-fullerene acceptor systems. Finally, this work provides guidelines for morphology optimization and synergistically promotes the fast development of efficient indoor OPVs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. Here we present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating Methods of Software Bill of Materials Generation to Enhance Nuclear Power Plant Cybersecurity

Instrumentation and control (I&C) systems in nuclear power plants (NPPs) are potential targets of cyberattacks and can prove deleterious for the safety of the NPPs. A Software Bill of Materials (SBOM) provides a detailed list of the various components and their dependencies in software, which helps in vulnerability and risk assessment for cyber hygiene and situational awareness. For an NPP, the process of generating an accurate SBOM report can be complex due to the legacy systems and firmware binaries involved. While most current SBOM tools are focused more on modern internet technology software, this research provides insights and guidelines for an NPP to generate an accurate and efficient SBOM. Here, the paper proposes a new methodology to help NPPs categorize software and use appropriate tools to generate SBOMs for their digital I&C systems.

SBOM↗
DOE OSTI2021

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. Here, the findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Estimating the value of jointly optimized electric power generation and end use: a study of ISO-scale load shaping applied to the residential building stock

A generation-to-load simulation estimated the impact, in terms of production costs and CO2 emissions, attributable to the joint optimization of electric power generation and flexible end uses to support increasing penetrations of renewable energy. Newly conceived, evaluated, and foundational in developing a U.S. National Standard was a transaction-less yet continuous demand response system based on a day-ahead optimum load shape (OLS) designed to encourage Internet-connected devices to autonomously and voluntarily explore options to favour lowest cost generators - without requiring two-way communications, personally identifiable information, or customer opt-in. Boundary conditions used for model calibration included historical weather, residential building stock construction attributes, home appliance and device empirical operating schedules, prototypical power distribution feeder models, thermal generator heat rates, startup and ramping constraints, and fuel costs. Results of an hourly-based annual case study of Texas indicate a 1/3 reduction in production costs and a 1/5 reduction in CO2 emissions are possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Do people spend travel time the way they think they would? a comparative study of generic and trip-specific travel time allocation using hybrid multiple discrete continuous (MDC) framework

Unlike driving alone, public transportation allows one to engage in extraneous activities while traveling – often referred to as travel-based multitasking. Research involving travel-based multitasking often relies on either generic (i.e. not related to an actual trip) or revealed (i.e. related to an executed trip) data. The data collected for this study provided a unique opportunity to compare the generic and trip-specific preferences while traveling on public transportation. To this end, the study develops two integrated choice and latent variable models with multiple discrete-continuous (MDC) kernels to simultaneously model the activity selection and the time allocation for generic and trip-specific data, respectively. According to the results the trip-specific data could identify more nuances in the travel-based multitasking behavior than the generic data. Finally, the heterogeneity identified by the developed models will be helpful for the transit operators in providing appropriate facilities (e.g. internet access, reading lights) along various transit corridors.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. Furthermore, this paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

14 SOLAR ENERGY↗
DOE OSTI2022

Wavelength conversion for single-photon polarization qubits through continuous-variable quantum teleportation

A quantum internet connects remote quantum processors that need to interact and exchange quantum signals over a long distance through photonic channels. However, these quantum nodes operate at the wavelength ranges unsuitable for long-distance transmission. Therefore, quantum wavelength conversion to telecom bands is crucial for long-distance quantum networks based on optical fiber. Here, we propose wavelength conversion devices for single-photon polarization qubits using continuous-variable quantum teleportation that can efficiently convert qubits between near-infrared (780–795 nm suitable for interacting with atomic quantum nodes) and telecom wavelength (1300–1500 nm suitable for long-distance transmission). The teleportation uses entangled photon fields (i.e., nondegenerate two-mode squeezed state) that can be generated by four-wave mixing in a rubidium atomic gas using a diamond configuration of atomic transitions. The entangled fields can be emitted in two orthogonal polarizations with locked relative phase, making them especially suitable for interfacing with single- photon polarization qubits. Furthermore, our work may pave the way for the realization of long-distance quantum networks.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Procrustean entanglement concentration in quantum-classical networking

The success of a future quantum internet will rest in part on the ability of quantum and classical signals to coexist in the same optical fiber infrastructure, a challenging endeavor given the orders of magnitude differences in flux of single-photon-level quantum fields and bright classical traffic. Here, we theoretically describe and experimentally implement Procrustean entanglement concentration for polarization-entangled states contaminated with classical light, showing significant mitigation of crosstalk noise in dense wavelength-division multiplexing. Our approach leverages a pair of polarization-dependent loss emulators to attenuate highly polarized crosstalk that results from imperfect isolation of conventional signals copropagating on shared fiber links. We demonstrate our technique both on the tabletop and over a deployed quantum local area network, finding a substantial improvement of two-qubit entangled state fidelity from approximately 75% to over 92%. This local filtering technique could be used as a preliminary step to reduce asymmetric errors, potentially improving the overall efficiency when combined with more complex error-mitigation techniques in future quantum repeater networks.

97 MATHEMATICS AND COMPUTING↗