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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Simulated Impact of Shortened Strings in Commercial and Utility-Scale Photovoltaic Arrays

The deliberate removal of photovoltaic modules from a string can occur for various reasons encompassing maintenance, measurements, theft, or failure, reducing that string length relative to others when replacement modules are not available and there are not any viable alternative makes and models that could be inserted. This phenomenon, delineated in our prior experimentally validated research, manifests two significant effects: (1) a shift in the ideal maximum power point and (2) the induction of potentially substantial reverse currents in the shortened strings at open-circuit voltage, VOC. However, the scalability and asymptotic limits of these observed behaviors concerning array size remained undetermined. In this study, we elucidate the operational dynamics of such arrays by manipulating two mismatch-contributing variables in simulated arrays of up to 900 strings: the number of removed modules per string (indicative of the level of mismatch, ranging up to 5) and the quantity of shortened strings (1 to 60). Simulation outcomes underscore that mismatch severity impacts array operation more than the proportion of shortened strings. This research delves into the practical ramifications of operating with shortened strings, including implications for low-irradiance operation and the manifestation of deleterious reverse currents (>35 A in specific cases), emphasizing the need for careful array configuration for optimal performance and safety in these implementations.

Energy & Fuels↗
DOE OSTI2024

Influence of Carboxymethyl Cellulose as a Thickening Agent for Glauber’s Salt-Based Low Temperature PCM

This work is focused on a novel, promising low temperature phase change material (PCM), based on the eutectic Glauber’s salt composition. To allow phase transition within the refrigeration range of temperatures of +5 °C to +12 °C, combined with a high repeatability of melting–freezing processes, and minimized subcooling, the application of three variants of sodium carboxymethyl cellulose (Na-CMC) with distinct molecular weights (700,000, 250,000, and 90,000) is considered. The primary objective is to optimize the stabilization of this eutectic PCM formulation, while maintaining the desired enthalpy level. Preparation methods are refined to ensure repeatability in mixing components, thereby optimizing performance and stability. Additionally, the influence of Na-CMC molecular weight on stabilization is examined through differential scanning calorimetry (DSC), T-history, and rheology tests. The PCM formulation of interest builds upon prior research in which borax, ammonium chloride, and potassium chloride were used as additives to sodium sulfate decahydrate (Glauber’s salt), prioritizing environmentally responsible materials. The results reveal that CMC with molecular weights of 250 kg/mol and 90 kg/mol effectively stabilize the PCM without phase separation issues, slowing crystallization kinetics. Conversely, CMC of 700 kg/mol proved ineffective due to the disruption of gel formation at its low gel point, hindering higher concentrations. Calculations of ionic concentration indicate higher Na ion content in PCM stabilized with 90 kg/mol CMC, suggesting increased ionic interactions and gel strength. A tradeoff is discovered between the faster crystallization in lower molecular weight CMC and the higher concentration required, which increases the amount of inert material that does not participate in the phase transition. After thermal cycling, the best formulation had a latent heat of 130 J/g with no supercooling, demonstrating excellent performance. This work advances PCM’s reliability as a thermal energy storage solution for diverse applications and highlights the complex relationship between Na-CMC molecular weight and PCM stabilization.

25 ENERGY STORAGE↗
DOE OSTI2023

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Na-Montmorillonite Edge Structure and Surface Complexes: An Atomistic Perspective

The edges of montmorillonite (MMT) react strongly with metals and organic matter, but the atomic structure of the edge and its surface complexes are not unambiguous since the experimental isolation of the edge is challenging. In this study, we introduce an atomistic model of a Na MMT edge that is suitable for classical molecular dynamics (MD) simulations, in particular for the B edge, a representative edge surface of 2:1 phyllosilicates. Our model possesses the surface groups identified through density functional theory (DFT) geometry optimizations performed with variation in the structural charge deficit and Mg substitution sites. The edge structure of the classical MD simulations agreed well with previous DFT-based MD simulation results. Our MD simulations revealed an extensive H-bond network stabilizing the Na-MMT edge surface, which required an extensive simulation trajectory. Some Na counter ions formed inner-sphere complexes at two edge sites. The stronger edge site coincided with the exposed vacancy in the dioctahedral sheet; a weaker site was associated with the cleaved hexagonal cavity of the tetrahedral sheet. The six-coordinate Na complexes were not directly associated with the Mg edge site. Our simulations have demonstrated the heterogeneous surface structures, the distribution of edge surface groups, and the reactivity of the MMT edge.

58 GEOSCIENCES↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2015

A microfluidic platform for precision small-volume sample processing and its use to size separate biological particles with an acoustic microdevice [Precision size separation of biological particles in small-volume samples by an acoustic microfluidic system]

Here, a major advantage of microfluidic devices is the ability to manipulate small sample volumes, thus reducing reagent waste and preserving precious sample. However, to achieve robust sample manipulation it is necessary to address device integration with the macroscale environment. To realize repeatable, sensitive particle separation with microfluidic devices, this protocol presents a complete automated and integrated microfluidic platform that enables precise processing of 0.15–1.5 ml samples using microfluidic devices. Important aspects of this system include modular device layout and robust fixtures resulting in reliable and flexible world to chip connections, and fully-automated fluid handling which accomplishes closed-loop sample collection, system cleaning and priming steps to ensure repeatable operation. Different microfluidic devices can be used interchangeably with this architecture. Here we incorporate an acoustofluidic device, detail its characterization, performance optimization, and demonstrate its use for size-separation of biological samples. By using real-time feedback during separation experiments, sample collection is optimized to conserve and concentrate sample. Although requiring the integration of multiple pieces of equipment, advantages of this architecture include the ability to process unknown samples with no additional system optimization, ease of device replacement, and precise, robust sample processing.

42 ENGINEERING↗
DOE OSTI2025

Frontal Slice Approaches for Tensor Linear Systems

Inspired by the row and column action methods for solving large-scale linear systems, in this work, we explore the use of frontal slices for solving tensor linear systems. In particular, this paper presents a novel approach for using frontal slices of a tensor $\mathcal{A}$ to solve tensor linear systems $\mathcal{A} ∗\mathcal{X} = \mathcal{B}$ where ∗ denotes the $t$-product. In addition, we consider variations of this method, including cyclic, block, and randomized approaches, each designed to optimize performance in different operational contexts. Our primary contribution lies in the development and convergence analysis of these methods. Experimental results on synthetically generated and real-world data, including applications such as image and video deblurring, demonstrate the efficacy of our proposed approaches and validate our theoretical findings.

Luo, Hengrui↗
DOE OSTI2024

Piezocatalytic ZnS:Mn 2+ Nanocrystals for Enhanced Organic Dye Degradation

Piezocatalysis, an emerging approach that harnesses mechanical energy to drive chemical reactions, has garnered significant attention due to its potential applications in diverse fields, particularly in environmental remediation. Its broader application, however, is often hindered by the low efficiency of existing piezocatalytic materials. Here, we report the synthesis of Mn 2+ -doped ZnS nanocrystals with improved piezoelectric properties using an emulsion-based colloidal assembly technique. Through well-controlled Mn 2+ doping, these nanocrystals demonstrate high piezocatalytic activity for degrading organic dyes under ultrasonic vibration. The optimal performance is achieved with 3% Mn 2+ doping, outperforming many existing piezocatalysts. Mechanistic studies reveal the generation of reactive oxygen species as the primary driving force for degradation. Notably, pre-excitation with UV light further boosts the piezocatalytic efficiency of these metal ion-doped ZnS nanocrystals by filling electron trap states, leading to improved overall performance. This research paves the way for developing high-performance piezocatalysts, expanding the potential of piezocatalysis for a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

BOPTEST as a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗
DOE OSTI2024

Measuring dark neutrinos and light using external and internal components of LArTPCs

The so-called MiniBooNE low-energy excess has been a long-standing questionfor beyond Standard Model (BSM) physics in the neutrino field. The ShortBaseline Neutrino (SBN) program was proposed to investigate and provide furtherinsights into this excess. The SBN program utilises Liquid Argon Time ProjectionChamber (LArTPC) technology, which allows for precise measurements of neutrinointeractions and holds great potential for addressing significant open questions inthe field. Within the SBN program, the Short Baseline Near Detector (SBND) plays acrucial role as one of the near detector, positioned just 110 meters from the neutrinosource. Being located on the surface, SBND is exposed to cosmic-ray background,and to mitigate this, the Cosmic Ray Tagger (CRT) system has been implemented toidentify and veto cosmic background events, thereby enhancing the purity of neutrinointeractions. Additionally, the CRT system can be used to search for BSM signatures,such as the dark neutrinos, which have been proposed to explain the MiniBooNElow-energy excess. This thesis presents a novel approach for searching for darkneutrinos using existing SBND CRT data, collected with a unique setup called theCRT Beam Telescope. An extended model-independent search based on the darkneutrino analysis is also presented. The sensitivity plot for the CRT Beam Telescopeis compared with an estimated MiniBooNE exclusion limit. Furthermore, this thesiscovers the commissioning work conducted for the SBND CRT system, which is vitalfor ensuring optimal performance. The LArTPC technology also benefits from liquidargon’s excellent scintillation properties, producing abundant scintillation light. Thisthesis describes a method developed to understand the detector response to thescintillation light produced across the LArTPC, enhancing our understanding of thedetector’s performance and facilitating more accurate measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Microfluidic-Based Robotic Sampling System for Radioactive Solutions

A novel microfluidic based robotic sampling system has been developed for sampling and analysis of liquid solutions in nuclear processes. This system couples the use of a microfluidic sample chip with a robotic system designed to allow remote, automated sampling of process solutions in-cell and facilitates direct coupling of the microfluidic sample chip with analytical instrumentation. This system provides the capability for near real time analysis, reduces analytical waste, and minimizes the potential for personnel exposure associated with traditional sampling methods. A prototype sampling system was designed, built and tested. System testing demonstrated operability of the microfluidic based sample system and identified system modifications to optimize performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Achieving 50% Energy Savings in New Schools, Advanced Energy Design Guides: K-12 Schools (Brochure)

This fact sheet summarizes recommendations for designing elementary, middle, and high school buildings that will result in 50% less energy use than conventional new schools built to minimum code requirements. The recommendations are drawn from the Advanced Energy Design Guide for K-12 School Buildings, an ASHRAE publication that provides comprehensive recommendations for designing low-energy-use school buildings (see sidebar). Designed as a stand-alone document, this fact sheet provides key principles and a set of prescriptive design recommendations appropriate for smaller schools with insufficient budgets to fully implement best practices for integrated design and optimized performance. The recommendations have undergone a thorough analysis and review process through ASHRAE, and have been deemed the best combination of measures to achieve 50% savings in the greatest number of schools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Achieving 50% Energy Savings in Office Buildings, Advanced Energy Design Guides: Office Buildings (Brochure)

This fact sheet summarizes recommendations for designing new office buildings that result in 50% less energy use than conventional designs meeting minimum code requirements. The recommendations are drawn from the Advanced Energy Design Guide for Small to Medium Office Buildings, an ASHRAE publication that provides comprehensive recommendations for designing low-energy-use office buildings with gross floor areas up to 100,000 ft2 (see sidebar). Designed as a stand-alone document, this fact sheet provides key principles and a set of prescriptive design recommendations appropriate for smaller office buildings with insufficient budgets to fully implement best practices for integrated design and optimized performance. The recommendations have undergone a thorough analysis and review process through ASHRAE, and have been deemed the best combination of measures to achieve 50% savings in the greatest number of office buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Design Fabrication and Characterization of High Density Silicon Photonic Components

Our burgeoning appetite for data relentlessly demands exponential scaling of computing and communications resources leading to an overbearing and ever-present drive to improve e ciency while reducing on-chip area even as photonic components expand to ll application spaces no longer satis ed by their electronic counterparts. With a high index contrast, low optical loss, and compatibility with the CMOS fabrication infrastructure, silicon-on-insulator technology delivers a mechanism by which e cient, sub-micron waveguides can be fabricated while enabling monolithic integration of photonic components and their associated electronic infrastructure. The result is a solution leveraging the superior bandwidth of optical signaling on a platform capable of delivering the optical analogue to Moore's Law scaling of transistor density. Device size is expected to end Moore's Law scaling in photonics as Maxwell's equations limit the extent to which this parameter may be reduced. The focus of the work presented here surrounds photonic device miniaturization and the development of 3D optical interconnects as approaches to optimize performance in densely integrated optical interconnects. In this dissertation, several technological barriers inhibiting widespread adoption of photonics in data communications and telecommunications are explored. First, examination of loss and crosstalk performance in silicon nitride over SOI waveguide crossings yields insight into the feasibility of 3D optical interconnects with the rst experimental analysis of such a structure presented herein. A novel measurement platform utilizing a modi ed racetrack resonator is then presented enabling extraction of insertion loss data for highly e cient structures while requiring minimal on-chip area. Finally, pioneering work in understanding the statistical nature of doublet formation in microphotonic resonators is delivered with the resulting impact on resonant device design detailed.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Technology Solutions Case Study: Moisture Management of High-Walls

Moisture management of high-R walls is important to ensure optimal performance. This case study, developed by Building America team Building Science Corporation, focuses on how eight high-R walls handle the three main sources of moisture—construction moisture, air leakage condensation, and bulk water leaks.

BSC↗
DOE OSTI2015

Finite Energy and Bounded Actuator Attacks on Cyber-Physical Systems

As control system networks are being connected to enterprise level networks for remote monitoring, operation, and system-wide performance optimization, these same connections are providing vulnerabilities that can be exploited by malicious actors for attack, financial gain, and theft of intellectual property. Much effort in cyber-physical system (CPS) protection has focused on protecting the borders of the system through traditional information security techniques. Less effort has been applied to the protection of cyber-physical systems from intelligent attacks launched after an attacker has defeated the information security protections to gain access to the control system. In this paper, attacks on actuator signals are analyzed from a system theoretic context. The threat surface is classified into finite energy and bounded attacks. These two broad classes encompass a large range of potential attacks. The effect of theses attacks on a linear quadratic (LQ) control are analyzed, and the optimal actuator attacks for both finite and infinite horizon LQ control are derived, therefore the worst case attack signals are obtained. The closed-loop system under the optimal attack signals is given and a numerical example illustrating the effect of an optimal bounded attack is provided.

Djouadi, Seddik M↗
DOE OSTI2015

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

WindSE

WindSE is a tool for modeling atmospheric fluid flow within a wind plant and performing optimization of the turbine positions or operating settings.

Source record↗