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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dual–Interface–Reinforced Flexible Perovskite Solar Cells for Enhanced Performance and Mechanical Reliability

Two key interfaces in flexible perovskite solar cells (f-PSCs) are mechanically reinforced simultaneously: one between the electron-transport layer (ETL) and the 3D metal-halide perovskite (MHP) thin film using self-assembled monolayer (SAM), and the other between the 3D-MHP thin film and the hole-transport layer (HTL) using an in situ grown low-dimensional (LD) MHP capping layer. The interfacial mechanical properties are measured and modeled. This rational interface engineering results in the enhancement of not only the mechanical properties of both interfaces but also their optoelectronic properties holistically. As a result, the new class of dual-interface-reinforced f-PSCs has an unprecedented combination of the following three important performance parameters: high power-conversion efficiency (PCE) of 21.03% (with reduced hysteresis), improved operational stability of 1000 h T90 (duration at 90% initial PCE retained), and enhanced mechanical reliability of 10 000 cycles n88 (number of bending cycles at 88% initial PCE retained). Furthermore, the scientific underpinnings of these synergistic enhancements are elucidated.

14 SOLAR ENERGY↗
DOE OSTI2020

Autonomous Strategies for Improved Performance and Reliability of Li‐Ion Batteries

Abstract The battery community has devoted significant effort to improve the performance and reliability of Li‐ion batteries through the development of new materials. The integration of autonomous function provides an alternative strategy to further extend performance. Autonomous material systems mimic the ability of biological systems to self‐protect, sense, regulate, and heal in response to damage and other environmental changes. When incorporated in batteries, these materials enable autonomic restoration of battery performance upon degradation, as well as autonomic shutdown or extinguishment in response to thermal runaway. Successful strategies for improving Li‐ion battery performance include controlled release of microencapsulated functional additives and the integration of self‐healing or adaptive interlayers and binders. These methods have been effective for stabilizing the solid electrolyte interface, preventing battery fires, restoring electrode conductivity, and suppressing Li dendrite growth. Challenges and opportunities exist in the further application of autonomous strategies in the battery environment. A greater variety of encapsulated additives with precise triggered‐release are needed to address specific battery degradation mechanisms. Incorporation of self‐healing polymers in solid‐state batteries facilitates improvement in interfacial and mechanical stability. The integration of autonomous strategies with battery management systems potentially enables enhanced control over multiple battery cycling parameters.

Zhao, Lihong↗
DOE OSTI2023

Harnessing Electrochemical‐Mechanical Couplings to Improve the Reliability of Solid‐State Batteries

Abstract One key barrier to using lithium‐metal anode batteries is that metal dendrites can penetrate solid electrolytes, causing short‐circuits and battery failures. It is established that this failure is likely caused by crack propagation due to electrodeposition‐induced stresses from lithium metal. This study explores ways to harness these electrochemical‐mechanical couplings to control dendrite growth and improve battery reliability using a phase‐field model and targeted fracture experiments. The results show that dendrite growth can be effectively mitigated by applying mechanical stresses or tailoring the material's fracture toughness. This study also outlines the requirements for compressive stress to halt or deflect dendrites as a function of the overpotential and discusses the role of microstructure in this process.

Monismith, Scott↗
DOE OSTI2020

Reliable machine prognostic health management in the presence of missing data

Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in-service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time-series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data-driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one-hot health state indicators are appended to the historical time series so that the model learns end-of-life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Furthermore, experiments on multivariate time-series datasets collected from real-world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long-term prognostics.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Importance of sample preparation on reliable surface characterisation of nano-objects: ISO Standard 20579-4

The international ISO Standard 20579-4, dealing with the history and preparation of nano-objects for surface analysis, has been developed to help address some of the replication and reproducibility issues caused by the fundamental nature of nano-objects. Although all types of samples requiring surface analysis need thoughtful preparation, nano-objects, for which many properties are controlled by their surfaces, present additional challenges in order to avoid variations and artefacts due to the handling and preparation of materials prior to analysis. This international standard is part of a series of standards related to preparation of samples for surface chemical analysis. Parts 1 and 2 of ISO Standard series 20579 address general issues that apply to many samples. Part 4 specifically considers issues that arise due to the inherent nature of nano-objects. Because of sensitivity to their environment, the standard indicates the minimum information that needs to be reported about the handling and preparation of nano-objects prior to surface analysis. This information should become part of sample provenance information that helps assure the reliability and usefulness of data obtained from surface-analysis in the context of the synthesis, processing and analysis history of a batch of material. Application of this standard can help address reproducibility and traceability issues associated with synthesis, processing, and characterization of nano-objects in research and commercial applications.

nano-object characterization, surface analysis, sa↗
DOE OSTI2025

Innovative Method for Reliable Measurement of PEM Water Electrolyzer Component Resistances

Understanding the sheet resistance of porous electrodes is essential for improving the performance of polymer electrolyte membrane (PEM) water electrolyzers and related technologies. Despite its importance, existing methods often fail to provide reliable and comprehensive data, especially for porous materials with complex morphologies and non‐uniform thicknesses. This study introduces a robust and straightforward method for determining the sheet resistance of porous electrodes using a novel probe concept based on industrial printed circuit board (PCB) technology. This probe measures resistance across ten distances, ranging from 250 µm to 2500 µm, enabling local mapping of resistance. The study focuses on the sheet resistance of key components in PEM water electrolyzers, including the gas diffusion layer (GDL), porous transport layer (PTL), and catalyst layers deposited on a membrane. Additionally, an image‐processing‐based method is presented to obtain the thickness distribution of the studied catalyst layers, facilitating a detailed analysis of the electrical in‐plane resistivity with thickness variations. Overall, this methodology has the potential to expedite material integration and bridge the gap between electrode engineering and single‐cell testing, thereby advancing the development of PEM water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

An approach to structural analysis using Moore-Shannon network reliability

We develop a formalism for approaching many questions dealing with network structure. The formalism is based on Shannon-Moore network reliability and on the Birnbaum definition of importance that uses it. The methods we suggest based on this formalism apply to a wide variety of dynamics on very general inter- action networks. They are designed to answer specific questions about how network structure affects particular aspects of the dynamics. We first introduce the concepts that make up the formalism, explain basic methods of using these concepts, and demonstrate applications to problems such as edge ranking.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Computer Modeling and Simulation: Increasing Reliability by Disentangling Verification and Validation

Verification and validation (V&V) of computer codes and models used in simulations are two aspects of the scientific practice of high importance that recently have been discussed widely by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to the model’s relation to the real world and its intended use. Because complex simulations are generally opaque to a practitioner, the Duhem problem can arise with verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or the model’s general inadequacy to its target should be blamed in the case of a failure. I argue that a clear distinction between computer modeling and simulation has to be made to disentangle verification and validation. Drawing on that distinction, I suggest to associate modeling with verification and simulation, which shares common epistemic strategies with experimentation, with validation. To explain the reasons for their entanglement in practice, I propose a Weberian ideal–typical model of modeling and simulation as roles in practice. I examine an approach to mitigate the Duhem problem for verification and validation that is generally applicable in practice and is based on differences in epistemic strategies and scopes. Based on this analysis, I suggest two strategies to increase the reliability of simulation results, namely, avoiding alterations of verified models at the validation stage as well as performing simulations of the same target system using two or more different models. In response to Winsberg’s claim that verification and validation are entangled I argue that deploying the methodology proposed in this work it is possible to mitigate inseparability of V&V in many if not all domains where modeling and simulation are used.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Inductive System for Reliable Magnesium Level Detection in a Titanium Reduction Reactor

The determination of the Magnesium level in a Titanium reduction retort by inductive methods is often hampered by the formation of Titanium sponge rings which disturb the propagation of electromagnetic signals between excitation and receiver coils. We present a new method for the reliable identification of the Magnesium level which explicitly takes into account the presence of sponge rings with unknown geometry and conductivity. The inverse problem is solved by a look-up-table method, based on the solution of the inductive forward problems for several tens of thousands parameter combinations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding the Value of Energy Storage for Power System Reliability and Resilience Applications

Abstract Purpose of Review The need for energy storage in the electrical grid has grown in recent years in response to a reduced reliance on fossil fuel baseload power, added intermittent renewable investment, and expanded adoption of distributed energy resources. While the methods and models for valuing storage use cases have advanced significantly in recent years, the value of enhanced resilience remains an open research question. Recent Findings The findings of the recent research indicate that energy storage provides significant value to the grid, with median benefit values for specific use cases ranging from under $10/kW-year for voltage support to roughly $100/kW-year for capacity and frequency regulation services. While the value of lost load is used widely to estimate the benefits of mitigating short-duration outages, reaching as high as $719/kilowatt-year, there is no consensus when it comes to monetizing the value of improving grid resilience. Summary This paper presents a use case taxonomy for energy storage and uses the taxonomy to conduct a meta-analysis of an extensive set of energy storage valuation studies. It reviews several approaches for monetizing reliability and resiliency services and presents a proposed approach for valuing resiliency for energy storage investments.

25 ENERGY STORAGE↗
DOE OSTI2022

Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature

Understanding the interaction between biological structures and nanoscale technologies, dubbed the nano-bio interface, is required for successful development of safe and efficient nanomedicine products. The lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine literature. As such, there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical translation. Here, we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as pharmaceuticals. We also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical practice. We, however, emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Standard operating procedures for the comprehensive and reliable analysis of cannabis terpenes

Terpenes are the primary determinants of cannabis flower aroma, and ongoing research tests their potential for impacting the overall experience. Frustratingly, despite the importance of terpenes in cannabis physiology and commercial uses, literature reports vary widely regarding the major constituents of volatile blends and the concentrations of individual terpenes. Here, in this article, we provide detailed descriptions of complementary approaches that will allow researchers to determine the identity and quantity of cannabis terpenes unequivocally and reliably. These standard operating procedures will guide decisions about which method to employ to address specific analytical goals. We are including two application examples to illustrate the utility of different approaches for tackling the analysis of terpenes in cannabis flower samples.

Polito, Joshua↗
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 OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2018

Addressing technical barriers for reliable, safe removal of fluoride from drinking water using minimally processed bauxite ores

Throughout the developing world, over 200 million people drink groundwater containing fluoride concentrations surpassing the World Health Organization's maximum recommended contaminant level (WHO-MCL) of 1.5 mg F - /L, resulting in adverse health effects ranging from mottled tooth enamel to debilitating skeletal fluorosis. Existing technologies to remove fluoride from water, such as reverse osmosis and filtration with activated alumina, are expensive and are not accessible for low-income communities. Our group and others have demonstrated that minimally-processed bauxite ores can remove fluoride to safe levels at a fraction of the cost of activated alumina. We report results from testing for some technical challenges that may arise in field deployment of this technology at large scale, particularly in a sufficiently robust manner for application in development contexts. Anticipating possible modes of failure and addressing these challenges in advance in the laboratory is particularly important for technologies for vulnerable communities where the opportunity to re-launch pilot projects is limited and small failures can keep solutions from the people that need them most. This work addresses three potential technical barriers to reliable removal of fluoride from drinking water with bauxite ore from Visakhapatnam, Andhra Pradesh, India. We evaluate competition from co-occurring ions, adsorption reversibility, and potability of the product water with regards to leaching of undesirable ions during treatment with various adsorbent materials including raw and thermally activated bauxite, and synthetic gibbsite (a simple model system). Under the conditions tested, the presence of phosphate significantly impacts fluoride adsorption capacity on all adsorbents. Sulfate impacts fluoride adsorption on gibbsite, but not on either bauxite adsorbent. Nitrate and silicate (as silicic acid), tested only with gibbsite, do not affect fluoride adsorption capacity. Both thermally activated bauxite and gibbsite show non-reversible adsorption of fluoride at a pH of 6. Raw bauxite leached arsenic and manganese in a TCLP leaching test at levels indicating the need for ongoing monitoring of treated water, but not precluding safe deployment of bauxite as a fluoride remediation technology. Understanding these phenomena is crucial to ensure field deployment over large diverse geographical areas with aquifers varying in groundwater composition, and for ensuring that the appropriate engineering processes are designed for field implementation of this innovation.

42 ENGINEERING↗
DOE OSTI2026

Field-based AFDD for refrigerant undercharge in residential HVAC systems: enhancing reliability through false alarm mitigation

This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.

False Alarm↗
DOE OSTI2019

Assessing the reliability of fast Fourier transform-based crystal plasticity simulations of a polycrystalline material near a crack tip

Crystal plasticity simulations enable a physically-based description of the micromechanical fields associated with microstructural attributes in polycrystalline materials. Fast Fourier transforms implementation of crystal plasticity (CP-FFT) in periodic unit cells offers increased computational efficiency compared to traditional finite element methods (CP-FEM). Albeit, questions may arise concerning the accuracy of the CP-FFT approach given its underlying spectral framework, specifically in regards to: potential artifacts due to violating the traction-free conditions at internal free surface within the unit cell, Gibbs phenomenon near regions with sharp changes in local mechanical properties, and representation of free surfaces under the constraints imposed by the requirement of periodicity of the unit cell. This paper presents a custom massively parallelized implementation of CP-FFT. A direct comparison of simulation results for polycrystalline deformation near a crack in a Ti55531 alloy is presented between CP-FEM and CP-FFT. The micromechanical fields and slip system level values are quantified and are similar between the two methods. Up to 10% difference in the stress fields is reported at the crack tip and this value dissipates moving away from the crack tip after 5 voxels (7 µm). In conclusion, the results point towards the reliable use of CP-FFT simulations to model polycrystalline deformation with caveats based on the treatment of the discussed artifacts.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Expanded graphite for thermal conductivity and reliability enhancement and supercooling decrease of MgCl2⋅6H2O phase change material

Highlights: • MgCl{sub 2}·6H{sub 2}O/EG composite PCMs show the superior thermal transfer ability. • 83.33 wt.% of MgCl{sub 2}·6H{sub 2}O encapsulated in composite PCM was obtained. • EG and nucleating agents decreased the supercooling degree. - Abstract: Expanded graphite (EG) is considered as a promising supporter for phase change material (PCM) due to its unique porous structure and excellent heat transfer ability. In the study, EG with different mass fractions (9 wt.%, 13 wt.%, 16.67 wt.%, and 20 wt.%) was respectively blended with MgCl{sub 2}·6H{sub 2}O. The thermal conductivity data of composite PCMs with 9 wt.%, 13 wt.%, 16.67 wt.%, and 20 wt.% of EG was respectively measured to be 0.942 W/m K, 1.053 W/m K, 1.354 W/m K and 1.658 W/m K. DSC analysis showed that 16.67 wt.% of EG decreased the degree of supercooling by 29.4 °C and that the addition of 3.0 wt.% SrCO{sub 3} further decreased the degree of supercooling by 17.9 °C. The maximum encapsulation weight percentage of MgCl{sub 2}·6H{sub 2}O reached 83.33 wt.% after 30 phase change cycles without significantly reducing its latent heat value, exhibiting the relatively stable thermal reliability.

36 MATERIALS SCIENCE↗