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At least 55 records · Page 3

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗

Daily operational impacts on battery degradation in heavy-duty electric drayage trucks

Battery aging is a critical factor influencing the performance, longevity, and cost of ownership of battery electric trucks (BETs). This paper presents a comprehensive evaluation of battery aging for two Li-ion battery chemistries, Nickel-Manganese-Cobalt (NMC) and Lithium-Iron-Phosphate (LFP), accounting for both cycling and calendar aging. In contrast to traditional methods that rely on simplified linear degradation models based on manufacturer-provided data, this study employs semi-empirical aging models calibrated to experimentally collected data. The models are integrated into a detailed vehicle simulation environment, enabling a comprehensive assessment of battery degradation under realistic operating conditions. A case study focusing on heavy-duty electric drayage truck operations in the Port of Savannah, GA, is presented to illustrate the impact on battery pack lifespan of: seasonal variations, daily operational activities, charging strategies, and battery storage conditions. The results illuminate the significance of the battery pack’s state of charge during stationary periods, such as overnight storage or weekend parking, on battery degradation and its potential implications for long-term vehicle viability. Additionally, the study explores how different operational and environmental factors affect battery degradation, offering critical insights into best battery charging and storage practices. Our results demonstrate that LFP outperforms NMC in terms of years of useful life; however, by utilizing charging strategies that minimize the amount of time the battery spends resting at high levels of state-of-charge, the lifespan of the battery pack that uses NMC can nonetheless be increased by more than a factor of two.

25 ENERGY STORAGE↗

Hybrid of multi‐dimensional fillers for thermally enhanced polyamide 12 composites fabricated by selective laser sintering

Abstract Selective laser sintering (SLS) is an additive manufacturing technology that can provide a novel strategy to manufacture complex polymer‐based composites with tailored properties. In this study, polyamide 12 (PA12) composite powders based on Al 2 O 3 , boron nitride (BN), and carbon nanotubes (CNTs) were prepared via a two‐step mixing approach. Morphology characterization techniques indicated that the fillers were uniformly dispersed in the PA12 matrix as expected. Under the optimum 3D‐printing parameters, the fabrication of SLS parts with high filler loading could be obtained. With 40 wt% Al 2 O 3 , 8 wt% BN, and 2 wt% CNT hybrid fillers, the thermal conductivity of PA12 composites could reach 1.09 W/mK. It could be attributed to the synergistic effect of multi‐dimensional fillers to form dense and continuous thermal conduction paths. The factors influencing the performance of PA12 composites such as melting and crystallization behaviors, dielectric properties, and mechanical properties were also discussed. The results show that these composites have favorable dielectric and mechanical properties. Overall, the PA12 composites fabricated by SLS technology in this study exhibit excellent thermal management ability.

Yuan, Yue↗

Extending the Brick schema to represent metadata of occupants

Here, energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants' presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants' demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Reducing Spontaneous Orientational Polarization via Semiconductor Dilution Improves OLED Efficiency and Lifetime

Spontaneous orientational polarization (SOP) in the electron-transport layer (ETL) of organic light-emitting diodes (OLEDs) is increasingly recognized as a key factor influencing their performance. Here, we show that SOP is dramatically reduced in the common electron-transport material 2,2',2"-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) by coevaporating it together with medium-density polyethylene. Eliminating SOP from the ETL of blue fluorescent OLEDs in this manner reduces their operating voltage by 0.5 V, increases their external quantum efficiency (EQE) by 30%, and leads to a threefold increase in device lifetime. We show that the EQE and lifetime improvements both originate from reduced exciton-polaron annihilation in the emissive layer, and that this leads to a functional relationship between the two quantities that can be used to quantify the rate of annihilation-induced degradation in the device. Furthermore, these results highlight a substantial opportunity to improve OLED performance by controlling SOP through semiconductor dilution and suggest that this capability can be used to systematically isolate and understand exciton-polaron degradation in the pursuit of stable blue OLEDs.

36 MATERIALS SCIENCE↗

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗

Advanced Pt-Based Core–Shell Electrocatalysts for Fuel Cell Cathodes

Proton-exchange membrane fuel cells (PEMFCs) are highly efficient energy storage and conversion devices. Thus, the platinum group metal (PGM)-based catalysts which are the dominant choice for the PEMFCs have received extensive interest during the past couple of decades. However, the drawbacks in the existing PGM-based catalysts (i.e., high cost, slow kinetics, poor stability, etc.) still limit their applications in fuel cells. The Pt-based core–shell catalysts potentially alleviate these issues through the low Pt loading with the associated low cost and the high corrosion resistance and further improve the oxygen reduction reaction’s (ORR’s) activity and stability. This Account focuses on the synthetic strategies, catalytic mechanisms, factors influencing enhanced ORR performance, and applications in PEMFCs for the Pt-based core–shell catalysts. We first highlight the synthetic strategies for Pt-based core–shell catalysts including the galvanic displacement of an underpotentially deposited non-noble metal monolayer, thermal annealing, and dealloying methods, which can be scaled-up to meet the requirements of fuel cell operations. Subsequently, catalytic mechanisms such as the self-healing mechanism in the Pt monolayer on Pd core catalysts, the pinning effect of nitrogen (N) dopants in N-doped PtNi core–shell catalysts, and the ligand effect of the ordered intermetallic structure in L10-Pt/CoPt core–shell catalysts and their synergistic effects in N-doped L10-PtNi catalysts are described in detail. Additionally, the core–shell structure in the Pt-based catalysts have two main effects for enhanced ORR performance: (i) the interaction between Pt shells and core substrates can tune the electronic state of the surface Pt, thus boosting the ORR activity and stability, and (ii) the outer Pt shell with modest thickness can enhance the oxidation and dissolution resistance of the core, resulting in improved durability. We then review the recent attempts to optimize the ORR performance of the Pt-based core–shell catalysts by considering the shape, composition, surface orientation, and shell thickness. The factors influencing the ORR performance can be grouped into two categories: the effect of the core and the effect of the shell. In the former, PtM core–shell catalysts which use different non-PGM element cores (M) are summarized, and in the latter, Pt-based core–shell catalysts with different shell structures and compositions are described. The modifications of the core and/or shell structure can not only optimize the intermediate-binding energetics on the Pt surface through tuning the strain of the surface Pt, which increases the intrinsic activity and stability, but also offer a significantly decreased catalyst cost. Finally, we discuss the membrane electrode assembly performance of Pt-based core–shell catalysts in fuel cell cathodes and evaluate their potential in real PEMFCs for light-duty and heavy-duty vehicle applications. Even though some challenges to the activity and lifetime in the fuel cells remain, the Pt-based core–shell catalysts are expected to be promising for many practical PEMFC applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The HUNTER Dynamic Human Reliability Analysis Tool: Development of a Module for Performance Shaping Factors

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) in communication with a variety of methods and tools. This paper explores how we developed one of the HUNTER modules: namely, the Individual module for evaluating performance shaping factors (PSFs). A PSF is any factor that influences human performance (e.g., workload or complexity). In the existing HRA, they are used to highlight human errors and adjust error probabilities. We consider the eight PSFs suggested in the Standardized Plant Analysis Risk-HRA (SPAR-H) method, a representative HRA method widely used in the nuclear field. To support our dynamic modeling using the eight SPAR-H PSFs, we reviewed the human performance literature and developed data-based mathematical models to rate and quantify PSFs in the context of dynamic HRA. We also designed the Individual module to consist of two functions: (1) the PSF qualification function for automatically or manually evaluating PSF levels, and (2) the PSF quantification function for dynamically or statically determining PSF multiplier values and integrating them to adjust human error probabilities (HEPs). How each function works in regard to the SPAR-H PSFs, and how the PSFs serve to adjust the HEPs, were investigated via literature review and are discussed in this paper.

99 GENERAL AND MISCELLANEOUS↗

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗

The HUNTER Dynamic Human Reliability Analysis Tool: Development of a Module for Performance Shaping Factors

Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) in communication with a variety of methods and tools. In this paper, how we have developed one of the HUNTER modules, the individual module for evaluating performance shaping factors (PSFs), is introduced. The PSF refers to any factor that influences human performance such as workload or complexity. It has been used for highlighting human errors and adjusting the error probabilities in the existing HRA. We consider the eight PSFs suggested in the Standardized Plant Analysis Risk-HRA (SPAR-H) method, which is the representative HRA method widely used in the nuclear field. To support our dynamic modeling using the eight SPAR-H PSFs, we reviewed human performance literature and developed data-based mathematical models to rate and quantify PSFs in the context of dynamic HRA. We also design the individual module composed of the two functions: 1) the PSF qualification function that automatically or manually evaluates a PSF level, and 2) the PSF quantification function that dynamically or statically determines the PSF multiplier values and integrate them to adjust human error probabilities (HEPs). How each function works with the SPAR-H PSFs and how the PSFs adjust the HEPs are investigated through literature and discussed in this paper.

99 GENERAL AND MISCELLANEOUS↗

A compact mechanical power take-off for wave energy converters: Design, analysis, and test verification

Power Take-off (PTO) is one of the most critical components in the ocean wave conversion. This paper introduces an innovative compact mechanical PTO using a ball screw mechanism and a Mechanical Motion Rectifier (MMR) to improve the energy conversion performance. The proposed MMR PTO converts bidirectional oscillatory motion of the ocean waves into unidirectional rotation. The detailed design and working principle of the MMR PTO are explicitly explained. Dynamic analysis is presented for the three most important factors that influence the performance, including the equivalent mass, the equivalent damping, and the excitation frequency. The analytical relationship between these factors and the disengaged freewheeling motion caused by the MMR mechanism is derived, and the analytical solution is verified through dry lab testing. Experimental results in bench tests show that compared with the traditional linear PTO which use the ball screw to drive the generator directly, the MMR PTO is more efficient because of the unique freewheeling motion, achieving a maximum 81.2% energy transfer efficiency. After integrating the prototype PTO with a single-body point absorber type of wave energy converter, testing in a water tank yields total PTO efficiency up to 62.4%, showing a very promising potential for real applications.

16 TIDAL AND WAVE POWER↗

Concepts to Support HRP Integration Using Publications and Modeling

Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.

Mindock, J.↗

Thermal Performance Testing of EMU and OSS Liquid Cooling Garments

A test was conducted to evaluate three factors influencing the thermal performance of liquid cooling garments (LCG): (1) the comparable thermal performance of an Oceaneering developed engineering evaluation unit (EEU) prototype LDG, (2) the effect of the thermal comfort undergarment (TCU), and (3) the performance of a torso or upper body only LCG configuration. To evaluate the thermal performance of each configuration a metabolic test was conducted, utilizing suited subjects to generate the metabolic heat. For this study three (3) test subjects of similar health and weight produced a metabolic load on the LDG configuration by either resting (300-600 BTU/hr), walking at a slow pace (1200 BRU/hr), and walking at a brisk pace (2200 BTU/hr), as outlined in Figure 1, the metabolic profile. During the test, oxygen consumption, heart rate, relative humidity, air flow, inlet and outlet air pressure, inlet and outlet air temperature, delta air temperature, water flow (100 lb/hr), inlet water temperature (64 F), delta water temperature, water pressure, core body temperature, skin temperature, and sweat loss data was recorded. Four different test configurations were tested, with one configuration tested twice, as outlined in Table 1. The test was conducted with the suit subjects wearing the Demonstrator Suit, pressurized to vent pressure (approximately 0.5 psig). The demonstrator suit has an integrated ventilation duct system and was used to create a relevant environment with a captured ventilation return, an integrated vent tree, and thermal insulation from the environment.

Rhodes, Richard↗

High performance polymer development

The term high performance as applied to polymers is generally associated with polymers that operate at high temperatures. High performance is used to describe polymers that perform at temperatures of 177 C or higher. In addition to temperature, other factors obviously influence the performance of polymers such as thermal cycling, stress level, and environmental effects. Some recent developments at NASA Langley in polyimides, poly(arylene ethers), and acetylenic terminated materials are discussed. The high performance/high temperature polymers discussed are representative of the type of work underway at NASA Langley Research Center. Further improvement in these materials as well as the development of new polymers will provide technology to help meet NASA future needs in high performance/high temperature applications. In addition, because of the combination of properties offered by many of these polymers, they should find use in many other applications.

Hergenrother, Paul M.↗

Dissimilar Material Joining via Interlocking Metasurfaces

Background The integration of dissimilar materials poses a significant challenge in engineering, necessitating innovative solutions for robust and reliable joining. Interlocking metasurfaces (ILMs) are a new joining technology comprising arrays of autogenous features patterned across two surfaces that interlock to form robust structural joints. Objective Here, this study elucidates the factors influencing the tensile performance of ILM joints formed between dissimilar materials. Methods We employed parametric optimization to identify optimal unit cell geometries for maximal yield strength based on the hypothesis that the elastic tensile properties of the materials are the primary determinants of tensile performance. Experimental validation was performed by mechanically testing the theorized optimal ILM geometry and a range of ILM geometries to capture the overall behavior trends of joints between two additively manufactured polymers, VeroPureWhite (VW) and RGDA8430-DM (8430). Results Experimental validation of optimized designs revealed that additional factors, e.g. flexural strength and localized plasticity, also strongly influenced the tensile performance of T-slot ILMs joining dissimilar materials. The proposed optimal design remained the best performer. Conclusions This study demonstrates the viability of ILMs as a joining method for dissimilar materials. ILMs can join dissimilar materials with no loss in joint yield strength compared to joints composed solely of the weaker of the two constitutive materials. ILMs demonstrated their potential as a versatile and effective joining technology in diverse engineering applications.

Elbrecht, Benjamin James [Sandia National Laborato↗

Elephants Sharing the Highway: Studying TCP Fairness in Large Transfers over High Throughput Links

Escalating bandwidth demand strains high-performance data networks, posing potential performance risks. TCP congestion control algorithms enhance reliability and optimize bandwidth usage. Network performance is influenced by factors such as AQM algorithms and router buffer size. In the context of constrained network resources, understanding how TCP flows share networks and the resulting performance impact is essential. This paper introduces insights into TCP fairness and performance involving a comparison of TCP CUBIC, Reno, Hamilton, and BBR versions 1 and 2 across real-world networks supporting high bandwidths of up to 25 Gbps. The research explores TCP behaviors with AQM algorithms like FIFO, FQ_CODEL, and RED, alongside diverse buffer sizes. Notably, findings reveal that manipulating buffers and queuing methods yields contrasting outcomes based on bandwidth. BBRv2 emerges as a superior fair algorithm, pivotal for swift transfers, particularly in scientific data scenarios. These results provide crucial guidance for future network design, ensuring equitable performance optimization.

Kiran, Mariam↗

Probabilistic simulation of the human factor in structural reliability

Many structural failures have occasionally been attributed to human factors in engineering design, analyses maintenance, and fabrication processes. Every facet of the engineering process is heavily governed by human factors and the degree of uncertainty associated with them. Factors such as societal, physical, professional, psychological, and many others introduce uncertainties that significantly influence the reliability of human performance. Quantifying human factors and associated uncertainties in structural reliability require: (1) identification of the fundamental factors that influence human performance, and (2) models to describe the interaction of these factors. An approach is being developed to quantify the uncertainties associated with the human performance. This approach consists of a multi factor model in conjunction with direct Monte-Carlo simulation.

Shah, Ashwin R.↗

Centaur Standard Shroud (CSS) Heated Altitude Jettison Tests

Altitude jettison tests, at a pressure of 20 torr (0.39 psia), were performed on the Centaur Standard Shroud (CSS) in a 100-foot diameter vacuum chamber. These jettison tests were part of a series of flight qualification tests which were performed on the new CSS system in preparation for the Helios and Viking missions. The first two tests subjected the CSS to a thermal cycle which simulated aerodynamic heating during ascent flight and the third test was performed at altitude pressure and in ambient temperature conditions. The purpose of the ambient temperature test was to provide base line data by which the separate machanical and thermal factors that influence jettison performance could be evaluated individually. The CSS was successfully jettisoned in each of the three tests. Also, thermal, stress, and structural deflection data were obtained which verified the analytical predictions of CSS response to flight environmental conditions and performance during jettison. In addition, much important information was obtained on critical CSS-to-payload clearance losses due to shell motions prior to and during jettison. The effectiveness of the separation system was successfully demonstrated at maximum flight temperatures.

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