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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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297 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Durable Antifog Coatings for Spacesuit Helmets

Maintaining a high level of visibility through the helmet of NASA Extravehicular Mobility Unit (EMU) is very difficult under inclement operational conditions. The interior of the helmet bubble will fog up in a high humidity / cold environment with high work rate breathing for extravehicular activity (EVA) that can last up to 8 hours. Currently used wipe on antifog solutions have problems with i) durability, requiring reapplication for every EVA, ii) can potentially get in the astronaut’s eye, impairing their vision, and iii) bottles of solution are a consumable that need to be continuously restocked. Luna Labs has developed a durable, transparent coating that is projected to maintain optical and antifogging properties for the lifetime of the equipment. Luna Labs has leveraged our established Gentoo™ sol-gel coating platform to create a robust, transparent, long-lasting, and antifogging coating. Additionally, the proposed sol-gel coating is thin (2-8 µm), low cost (cents/ft 2 ), and easy to apply while providing excellent abrasion durability and antifogging properties. Luna Labs has produced a coating formulation to possess antifogging properties so that moist air will wet out, forming a microscopically thin sheet of water that does not scatter light. A custom superhydrophilic additive which crosslinks into the durable coating matrix provides superb antifogging properties. This non-scratch transparent coating that will provide continuous antifogging efficacy for >8 hours and environmental durability to last the lifetime of coated component. This technology is relevant to NASA spacesuit applications, as well as for automobiles, aircraft, eyewear, SCUBA masks or any other applications that require transparent antifogging properties.

Spacesuit↗
DOE OSTI2024

Study of Mechanical Grinding Effects on Niobium Surface

Mechanical grinding is commonly employed to eliminate surface defects such as scratches and pits from niobium cavity surfaces or sheets before cavity fabrication. Subsequently, chemically buffered polishing or electropolishing is often utilized to completely remove residues of the polishing media and any defects induced by mechanical grinding, ensuring a pristine surface. In this study, we conducted a systematic investigation to assess the influence of mechanical grinding using silicon carbide and aluminum oxide polishing media on niobium surfaces. Additionally, the study examines the effects of post-mechanical grinding chemical treatments on surface quality.

Chouhan, V.↗
DOE OSTI2024

Surface treatment of a sulfide glass solid electrolyte layer

Chemically treating ionically conductive sulfide glass solid electrolyte separators or separator layers can improve performance. In particular, treatment involving chemically etching a surface or surface region of the sulfide glass separator to blunt, lessen or remove edge defects or surface flaws, and/or to enhance surface smoothness is cost effective, reliable and well suited for high production environments compared to physical methods of removing scratches or smoothing surfaces, such as mechanical grinding and polishing.

Visco, Steven J.↗
DOE OSTI2024

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.

commercial buildings↗
DOE OSTI2025

RINO: Renormalization Group Invariance with No Labels

A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.

Hao, Zichun [Caltech] (ORCID:0000000256244907)↗
DOE OSTI2025

Control System Upgrade for Battery State-of-Charge Indications

The Advanced Test Reactor (ATR) Complex at Idaho National Laboratory (INL) relies on Battery Backed Power (BBP) systems and Uninterruptible Power Supplies (UPS) to ensure continuous power supply to critical components. This project aims to enhance the reliability and functionality of the battery monitoring and control systems by updating the State-of-Charge (SOC) system, Programmable Logic Controller (PLC), and Human-Machine Interface (HMI) for the nuclear safety-related battery banks. The current system, while functional, has areas for improvement, particularly in recharging calculations and alarm functions. The project objectives include developing flow charts, programming the new PLC and HMI, conducting bench tests, and updating design documentation. Additionally, the project ensures compliance with safety standards, develops training materials, creates comprehensive documentation, and integrates seamlessly with existing ATR infrastructure. The new SOC system is designed to be scalable for future upgrades, improve efficiency, enhance data accuracy, implement redundancy features, and achieve project goals within budget constraints while considering environmental impact. The methodology involved familiarizing with BBP and UPS systems, collecting current readings, rescaling signals, learning ladder logic, and updating the HMI. The transition from SLC 5/03 PLC using RS Logix 500 to CompactLogix 5380 using Studio 5000 was a key step. Despite challenges in transferring outdated PLC ladder logic and HMI code, starting from scratch led to a more accurate and efficient monitoring system, contributing to improved safety and operational efficiency. The project is currently awaiting approval of the Engineering Calculation and Analysis Report (ECAR) before implementation.

42 - ENGINEERING↗