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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Imaging Spectrometry-Concepts and System Tradeoffs

The concept of imaging spectrometry is finding broad application in scientific instrumentation for earth-based, airborne, and space applications. An imaging spectrometer is characterized by the combination of imaging with complete sampling in the spectral domain. In so doing, material identification can be accomplished and displayed in conjunction with the conventional recognizable image. An image spectrometer incorporates a wide variety of techniques, including focal plane arrays, imaging and spectrometer optics, and spectral dispersing devices. The design of a successful system involves a complex set of tradeoffs incorporating the properties and limitations of the various technologies. For applications in the infrared, additional technologies such as focal plane cooling are required, and the other technologies present more limitations and constraints.

Herring, M.↗
DOE OSTI2018

Mass spectrometry-based proteomics for system-level characterization of biological responses to engineered nanomaterials

The widespread applications of engineered nanomaterials or nanotechnology make the characterization of biological responses to nanomaterials as an important area of research. The applications of omics-based approaches such as mass spectrometry-based proteomics have revealed new insights into the cellular responses upon exposure to nanomaterials, including how nanomaterials interact and alter cellular pathways. For instance, a redox-dependent response is often associated with exposure of nanomaterials, which leads to the generation of reactive oxygen species. In this review, we discuss the general quantitative proteomics approaches, with an emphasis on redox proteomics and their applications for characterizing the biological responses to nanomaterials.

Zhang, Tong↗
DOE OSTI2015

Development of nanosecond time-resolved infrared detection at the LEAF pulse radiolysis facility

When coupled with transient absorption spectroscopy, pulse radiolysis, which utilizes high-energy electron pulses from an accelerator, is a powerful tool for investigating the kinetics and thermodynamics of a wide range of radiation-induced redox and electron transfer processes. The majority of these investigations detect transient species in the UV, visible, or near-IR spectral regions. Unfortunately, the often-broad and featureless absorption bands in these regions can make the definitive identification of intermediates difficult. Time-resolved vibrational spectroscopy would offer much improved structural characterization, but has received only limited application in pulse radiolysis. In this paper, we describe in detail the development of a unique nanosecond time-resolved infrared (TRIR) detection capability for condensed-phase pulse radiolysis on a new beam line at the LEAF facility of Brookhaven National Laboratory. The system makes use of a suite of high-power, continuous wave external-cavity quantum cascade lasers as the IR probe source, with coverage from 2330-1051 cm⁻¹. The response time of the TRIR detection setup is ~40 ns, with a typical sensitivity of ~100 µOD after 4-8 signal averages using a dual-beam probe/reference normalization detection scheme. As a result, this new detection method has enabled mechanistic investigations of a range of radiation-induced chemical processes, some of which are highlighted here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Synthesis and characterization of [BMIM]bromide using microwave-assisted organic synthesis method and its application for dissolution of palm empty fruit bunch

The decrease of cellulose crystallinity index of palm empty fruit bunch is crucial for the next application of cellulose as raw material for various biofuel and its derivatives. The aim of this research is to decrease the cellulose crystallinity index of palm empty fruit bunch using 1-butyl-3-methylimidazoliumbromide or [BMIM] bromide which has been synthesized using Microwave-Assisted Organic Synthesis (MAOS) method. Conventional reaction method has also been carried out to synthesize [BMIM]bromide for comparison as well. The characterization of synthesized product using FTIR, {sup 1}H-NMR, {sup 13}C-NMR and LC-MS showed that these reactions have been carried out successfully. The results showed that MAOS method is up to 90% faster in producing [BMIM]bromide compare to the conventional method. The application of [BMIM]bromide for dissolution of palm empty fruit bunch showed that cellulose and lignin could be extracted using stirring process for 20 hours. The decrease of cellulose crystallinity index and its morphology changes were identified using FTIR and Scanning Electron Microscope.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Salt Hydrate Eutectic Mixtures for Near-ambient Thermal Energy Storage Applications

In this work, we synthesized and characterized Na2SO4.10H2O – Na2CO3.10H2O eutectic salt hydrate phase change material (PCM) using Differential Scanning Calorimetry (DSC), Attenuated total reflectance infrared spectroscopy (ATR-IR), and X-ray Diffraction (XRD) techniques. The eutectic point of the PCM mixture was predicted based on binary eutectic effect and ion exchange theory. The XRD and ATR-IR results indicate that the eutectic PCM mixture was formed based on only physical mixing without chemical interaction. Moreover, DSC results showed that the eutectic concentration is 50wt% Na2SO4.10H2O – 50wt% Na2CO3.10H2O with melting temperature of 25.18°C and melting enthalpy of 187.46 J/g. Also, 78% of melting enthalpy was recoverable after 10 melt/freeze cycles. This eutectic mixture has the potential to be used in building thermal comfort applications.

Gluesenkamp, Kyle↗
DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Field evaluation of zone temperature response to control actions in cooling systems of small and medium-sized office buildings

The response of zone temperature to control actions in heating, ventilation, and air conditioning (HVAC) systems, known as zone temperature response, has been a central focus of building control research owing to its crucial role in determining control performance. However, existing studies often overlook the representativeness of the buildings being studied, resulting in unclear generalizations. In addition, those studies tend to focus on a single aspect of the response. Furthermore, this paper provides the first comprehensive characterization of zone temperature response applicable to a clearly defined building sector—small and medium-sized office (SMO) buildings (<5000 m 2 ) in the US. Specifically, two representative SMO buildings, selected based on the US Department of Energy’s commercial prototype buildings, were studied. Field tests were conducted over a 2-month period during summer, and the collected data were analyzed with two key metrics—delay time and nonlinearity index—to quantify zone temperature response, capturing both short- and long-term patterns. Beyond this quantitative characterization, the analysis reveals that the HVAC system type, rather than factors like floor area or zone location, is the primary determinant of the zone temperature response. Drawing on the field test results, we recommend that building control strategies monitor zone temperatures at intervals shorter than 10 minutes, configure controls independently for VAV- and RTU-served zones, and implement nonlinear methods at the zone level—particularly for VAV zones—rather than across the entire building.

Building control↗
DOE OSTI2016

Static and Dynamic Frequency Scaling on Multicore CPUs

Dynamic voltage and frequency scaling (DVFS) adapts CPU power consumption by modifying a processor’s operating frequency (and the associated voltage). Typical approaches employing DVFS involve default strategies such as running at the lowest or the highest frequency, or observing the CPU’s runtime behavior and dynamically adapting the voltage/frequency configuration based on CPU usage. In this paper, we argue that many previous approaches suffer from inherent limitations, such as not account- ing for processor-specific impact of frequency changes on energy for different workload types. We first propose a lightweight runtime-based approach to automatically adapt the frequency based on the CPU workload, that is agnostic of the processor characteristics. We then show that further improvements can be achieved for affine kernels in the application, using a compile-time characterization instead of run-time monitoring to select the frequency and number of CPU cores to use. Our framework relies on a one-time energy characterization of CPU-specific DVFS profiles followed by a compile-time categorization of loop-based code segments in the application. These are combined to determine a priori of the frequency and the number of cores to use to execute the application so as to optimize energy or energy-delay product, outperforming runtime approach. Extensive evaluation on 60 benchmarks and five multi-core CPUs show that our approach systematically outperforms the powersave Linux governor, while improving overall performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Truck Technology Efficiency Assessment (TTEA) Project (Final Report)

Medium- and heavy-duty trucks and buses are responsible for over 28% of the energy used and emissions generated in highway transportation, and class 8 tractor-trailers operating in long-haul and regional cargo transport are responsible for about 75% of all fuel consumed by commercial trucks. The vehicle miles traveled (VMT) for trucks is expected to increase at a rate significantly outpacing passenger VMT growth, which will result in a steady rise in the percentage of energy consumption (and emissions) attributable to trucks over the coming decades. These facts have sparked significant recent interest in truck fuel efficiency in the transportation community. Although fuel economy regulations in the United States have historically focused on passenger cars, recent legislation requiring new standards for fuel economy in medium- and heavy-duty trucks aims to increase the efficiency of trucks as well. The development of regulations for truck fuel efficiency is quite challenging, however, since vehicle usage and configurations vary substantially among the very diverse set of trucking applications. Fuel economy is very strongly linked to the particular drive cycles followed by a given truck, as are the gains in efficiency that can realized by implementing new technologies. As demonstration of this fact, a technology that provides significant fuel efficiency gains for one trucking application may yield little improvement or could even be detrimental to fuel economy in a different trucking application. It is therefore critical that the usage of each application be well understood and carefully evaluated to select the set of technologies that can provide the greatest benefits for each application. Although it is well known that drive cycle data representative of a vehicle or fleet’s usage is crucial for an accurate evaluation of fuel economy benefits or to identify an optimum set of technologies to reduce fuel consumption, detailed drive cycle data of trucks are not readily available for many applications. The Oak Ridge National Laboratory (ORNL) has collected a rather extensive set of truck duty cycle data in an effort to characterize the usage of several trucking applications. During the first phase of ORNL’s duty cycle data collection activities, drive cycle measurements were made from six tractor-trailers during normal operations in a regional commercial shipping fleet, operating primarily in the southeastern U.S., for a period of a full year. This data, contained in the Heavy Truck Duty Cycle (HTDC) project database, was analyzed in detail for the current research effort in the Truck Technology Efficiency Assessment project. Based on the high fidelity HTDC duty cycle data, the results of this study are therefore highly representative of tractor-trailer operations for this type of regional, freeway-dominant trucking application.

LaClair, Tim J.↗
NASA NTRS1992

Resource constrained design of artificial neural networks using comparator neural network

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Wah, Benjamin W.↗
DOE OSTI2015

Synthesis and characterization of Fe{sub 3}O{sub 4}: Porous carbon nanocomposites for biosensor application

Fe{sub 3}O{sub 4}:Porous carbon (Fe{sub 3}O{sub 4}:PC) nano-magnetic composites were prepared by using different weight fractions of acid treated PC by the chemical co-precipitation route and annealed at 573 K, 773 K and 973 K temperatures in inert N{sub 2} gas atmosphere for 2 hrs to obtain desired stoichiometry of nanocomposites. The structural, morphological and magnetic properties of these composites were characterized by powder XRD, TEM, EPR and VSM analytical techniques. The crystallinity of the composites, g-value and spin concentration increases with increasing annealing temperature. TEM images confirmed the formation of nanosized ferrite nanoprticles whose size increases from 23 nm to 54 nm on increasing annealing temperature. Porous carbon increases porosity, coercivity and reduces saturation magnetization of these prepared nanocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Critical Review of LPBF Metal Print Defects Detection: Roles of Selective Sensing Technology

The integrative potential of LPBF-printed parts for various innovative applications depends upon the robustness and infallibility of the part quality. Eliminating or sufficiently reducing factors contributing to the formation of defects is an integral step to achieving satisfiable part quality. Significant research efforts have been conducted to understand and quantify the triggers and origins of LPBF defects by investigating the material properties and process parameters for LPBF-printed geometries using various sensing technologies and techniques. Frequently, combinations of sensing techniques are applied to deepen the understanding of the investigated phenomena. The main objectives of this review are to cover the roles of selective sensing technologies by (1) providing a summary of LPBF metal print defects and their corresponding causes, (2) informing readers of the vast number and types of technologies and methodologies available to detect defects in LPBF-printed parts, and (3) equipping readers with publications geared towards defect detection using combinations of sensing technologies. Due to the large pool of developed sensing technology in the last few years for LPBF-printed parts that may be designed for targeting a specific defect in metal alloys, the article herein focuses on sensing technology that is common and applicable to most common defects and has been utilized in characterization for an extended period with proven efficiency and applicability to LPBF metal parts defect detection.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic soils for ecological and synthetic biology applications

Soils are heterogeneous and dynamic systems characterized by complex physical, chemical, and biological interactions. Understanding these interactions is critical, as they influence plant productivity, global biogeochemical cycles, and ecosystem resilience. While ecologists have long studied soils in field, greenhouse, and laboratory settings, their complexity and heterogeneity make it challenging to pinpoint key properties driving biological processes and derive mechanistic insights. Advancements in synthetic biology, which seeks to engineer and control biological processes in soils, have increased the demand for standardized and controllable experimental platforms. These platforms, referred to here as ‘synthetic soils’, are systems designed to reproduce selected physicochemical characteristics of natural soils in a simplified and defined format, allowing scientists to systematically change soil physicochemical properties (i.e. texture, mineralogy, pH) to study how biological components (i.e. microbes, plants, soil fauna, etc.) respond to, modify, or interact within these controlled environments. This review explores existing synthetic soils, their advantages, limitations, and applications in ecology and synthetic biology, and discusses potential directions for their future development.

artificial soils↗
NASA NTRS1992

Reliability evaluation methodology for NASA applications

Liquid rocket engine technology has been characterized by the development of complex systems containing large number of subsystems, components, and parts. The trend to even larger and more complex system is continuing. The liquid rocket engineers have been focusing mainly on performance driven designs to increase payload delivery of a launch vehicle for a given mission. In otherwords, although the failure of a single inexpensive part or component may cause the failure of the system, reliability in general has not been considered as one of the system parameters like cost or performance. Up till now, quantification of reliability has not been a consideration during system design and development in the liquid rocket industry. Engineers and managers have long been aware of the fact that the reliability of the system increases during development, but no serious attempts have been made to quantify reliability. As a result, a method to quantify reliability during design and development is needed. This includes application of probabilistic models which utilize both engineering analysis and test data. Classical methods require the use of operating data for reliability demonstration. In contrast, the method described in this paper is based on similarity, analysis, and testing combined with Bayesian statistical analysis.

Taneja, Vidya S.↗
DOE OSTI2025

A Flang Plugin for Fortran Feature Characterization

As new compute systems are developed, there is still a need to compile and execute codes authored in Fortran on these leading edge systems. In order to achieve this, development of compilers that support the latest hardware is continuously under development. Though the specification of Fortran is extensive, it is helpful to compiler authors to be able to prioritize the development of key features in order to get certain codes deemed important, e.g., applications of interest to leadership computing facilities, executable on leading edge compute systems. Identifying key features though is largely done through querying software experts or users of the Fortran applications of interest, who then manually report what features are and are not present. This exercise can both time consuming and error prone. To automate this process, we present a compiler plugin to Flang, the Fortran frontend for LLVM. This plugin is a tool that operates on the parse tree representation generated by Flang and detects key features based on walking parse tree nodes that correspond to features of interest. We show the result of our tool on four applications, three of which were manually profiled by software experts. We show the discrepancies between our tool and the manual characterization of the three applications, as well as generate a characterization for an application not yet profiled. We intend to open-source our tool in order to invite the community to benefit from the tool and make contributions for other features.

Cabrera, Anthony [ORNL]↗
DOE OSTI2025

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗
DOE OSTI2018

Characterization of a Low-Cost Multi-Parameter Sensor for Solar Resource Applications

Low-cost multi-parameter sensing and measurement devices enable cost-effective monitoringof the functional, operational reliability, efficiency, and resiliency of the electrical grid.The National Renewable Research Laboratory (NREL) Solar Radiation Research Laboratory (SRRL), in collaboration with Arable Labs Inc., deployed Arable Lab's Mark multi-parameter sensor system. The unique suite of system sensors measures the downwelling and upwelling shortwave solar resource and longwave radiation, humidity, air temperature, and ground temperature. This studydescribes the shortwave calibration, characterization, and validation of measurement accuracy of this instrument by comparison with existing instruments that are part of NREL-SRRL's Baseline Measurement System.

14 SOLAR ENERGY↗
DOE OSTI2021

Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments

Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.

36 MATERIALS SCIENCE↗