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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mitigating leaks in membranes

Two-dimensional material based filters, their method of manufacture, and their use are disclosed. In one embodiment, a membrane may include an active layer including a plurality of defects and a deposited material associated with the plurality of defects may reduce flow therethrough. Additionally, a majority of the active layer may be free from the material. In another embodiment, a membrane may include a porous substrate and an atomic layer deposited material disposed on a surface of the porous substrate. The atomic layer deposited material may be less hydrophilic than the porous substrate and an atomically thin active layer may be disposed on the atomic layer deposited material.

Karnik, Rohit N.↗
DOE OSTI2023

Microstructure geometry for thermal barrier coatings to mitigate CMAS attack

Thermal barrier coatings and methods to make such coatings present improved resistance to CMAS infiltration. The method for forming a thermal barrier coating includes applying a layer of the thermal barrier coating to a component having a surface, forming a plurality of first channels in the thermal barrier coating, and forming a plurality of second channels in the thermal barrier coating. The first channels extend through a thickness of the thermal barrier coating from an interface with the surface of the component to a free surface opposite the interface. The second channels are disposed between the free surface and the interface and extending lengthwise generally parallel to the free surface of the thermal barrier coating, wherein the thermal barrier coating comprises a material comprising yttrium aluminum garnet (YAG) or yttria stabilized zirconia (YSZ).

Jordan, Eric↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

PV Soiling Losses: Measurements, Modeling, and Mitigation Strategies

An update will be provided on NREL's ongoing work to address the range of soiling challenges the PV community is facing. First, results will be shown on the efforts to develop a low-cost and low-maintenance soiling measurement sensor. Second, the latest NREL soiling map will be demonstrated as well as how the PVfleets database is enabling regular improvement to the map. Additionally, PVfleets is uncovering challenges with pollen and other bio-soiling in rainy regions in the southeast United States. Finally new results will be presented for improvement of automated algorithms to extract soiling losses from PV data.

ENGINEERING,SOLAR ENERGY↗
DOE OSTI2024

Threats to DERs and Tools to Mitigate Them

As the pace of renewable energy development increases, so does the challenge and opportunity to develop innovative solutions to secure renewable technologies. Four national laboratories National Renewable Energy Laboratory, Sandia National Laboratories, Pacific Northwest National Laboratory, and Idaho National Laboratory are working together to increase cybersecurity maturity levels for solar stakeholders. INL will discuss a DER threat briefing and operator tools and training developed under the Securing Solar for the Grid (S2G) project.

14 SOLAR ENERGY↗
DOE OSTI2024

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect of risk awareness involves modelling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. These infrequent occurrences present a formidable challenge for data-driven methods aiming to capture such risky events accurately. While risk-aware RL techniques do exist, they suffer from high variance estimation due to the inherent data scarcity. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value distribution. To achieve this, we formulate the extreme values of the state-action value function distribution as parameterized distributions, drawing inspiration from the principles of extreme value theory (EVT). We propose an extreme value theory based actor-critic approach, namely, Extreme Valued Actor-Critic (EVAC) which effectively addresses the issue of infrequent occurrence by leveraging EVT-based parameterization. Importantly, we theoretically demonstrate the advantages of employing these parameterized distributions in contrast to other risk-averse algorithms. Our evaluations show that the proposed method outperforms other risk averse RL algorithms on a diverse range of benchmark tasks, each encompassing distinct risk scenarios.

Wang, Yu↗