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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Processing and Properties of Fiber Reinforced Polymeric Matrix Composites: Processing Robustness of IM7/PETI Polyimide Composites - Part 2

The processability of a phenylethynyl terminated imide (PETI) resin matrix composite was investigated. Unidirectional prepregs were made by coating an N-methylpyrrolidone solution of the amide acid oligomer onto unsized IM7. Two batches of prepregs were used: one was made by NASA in-house, and the other was from an industrial source. The composite processing robustness was investigated with respect to the effect of B-staging conditions, the prepreg shelf life, and the optimal processing window. Rheological measurements indicated that PETI's processability was only slightly affected over a wide range of B-staging temperatures (from 250 C to 300 C). The open hole compression (OHC) strength values were statistically indistinguishable among specimens consolidated using various B-staging conditions. Prepreg rheology and OHC strengths were also found not to be affected by prolonged (i.e., up to 60 days) ambient storage. An optimal processing window was established using response surface methodology. It was found that IM7/PETI composite is more sensitive to the consolidation temperature than to the consolidation pressure. A good consolidation was achievable at 371 C/100 Psi, which yielded an OHC strength of 62 Ksi at room temperature. However, processability declined dramatically at temperatures below 350 C.

Hou, Tan-Hung↗
NASA NTRS2013

Closed-Loop Process Control for Electron Beam Freeform Fabrication and Deposition Processes

A closed-loop control method for an electron beam freeform fabrication (EBF(sup 3)) process includes detecting a feature of interest during the process using a sensor(s), continuously evaluating the feature of interest to determine, in real time, a change occurring therein, and automatically modifying control parameters to control the EBF(sup 3) process. An apparatus provides closed-loop control method of the process, and includes an electron gun for generating an electron beam, a wire feeder for feeding a wire toward a substrate, wherein the wire is melted and progressively deposited in layers onto the substrate, a sensor(s), and a host machine. The sensor(s) measure the feature of interest during the process, and the host machine continuously evaluates the feature of interest to determine, in real time, a change occurring therein. The host machine automatically modifies control parameters to the EBF(sup 3) apparatus to control the EBF(sup 3) process in a closed-loop manner.

Taminger, Karen M.↗
NASA NTRS2016

In-Process Thermal Imaging of the Electron Beam Freeform Fabrication Process

Researchers at NASA Langley Research Center have been developing the Electron Beam Freeform Fabrication (EBF3) metal additive manufacturing process for the past 15 years. In this process, an electron beam is used as a heat source to create a small molten pool on a substrate into which wire is fed. The electron beam and wire feed assembly are translated with respect to the substrate to follow a predetermined tool path. This process is repeated in a layer-wise fashion to fabricate metal structural components. In-process imaging has been integrated into the EBF3 system using a near-infrared (NIR) camera. The images are processed to provide thermal and spatial measurements that have been incorporated into a closed-loop control system to maintain consistent thermal conditions throughout the build. Other information in the thermal images is being used to assess quality in real time by detecting flaws in prior layers of the deposit. NIR camera incorporation into the system has improved the consistency of the deposited material and provides the potential for real-time flaw detection which, ultimately, could lead to the manufacture of better, more reliable components using this additive manufacturing process.

Taminger, Karen M.↗
DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

[Challenges and Opportunities in] Process Modeling of Intensified Chemical Processes

Before the benefits of process intensification can be fully realized, a multitude of advances in process modeling will be necessary. Process intensification (PI) has the potential to speed development of chemical engineering innovations. Among its many benefits, PI can increase the driving forces (physical and/or chemical) for transport phenomena, separations, and/or reactions in chemical processes, and inspire novel process configurations with major technical and economic benefits. Examples of PI applications include integrated process units such as heat exchange networks, sorption- or membrane-enhanced reactors, and dividing-wall distillation columns, among others [AIChE(2017)].

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Additive Manufacturing Model-Based Process Metrics: Reduced Order Modeling of the Laser Powder Bed Fusion Process

The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. In this work, we describe a fully parallel reduced order modeling approach that has been developed to evaluate the evolution of AM processes, termed the AM moment measure method. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology and terminology of the approach will be described, and computed build maps will be calculated and compared for various laser powder bed fusion (LPBF) builds of Ti-6Al-4V. Such comparative results develop understanding of how the sequential process actions can affect the LPBF-AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
DOE OSTI2017

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.

316L stainless steel↗
DOE OSTI2018

Spontaneous Charge Separation and Sublimation Processes are Ubiquitous in Nature and in Ionization Processes in Mass Spectrometry

Ionization processes have been discovered by which small and large as well as volatile and nonvolatile compounds are converted to gas-phase ions when associated with a matrix and exposed to sub-atmospheric pressure. Here, we discuss experiments further defining these simple and unexpected processes. Charge separation is found to be a common process for small molecule chemicals, solids and liquids, passed through an inlet tube from a higher to a lower pressure region, with and without heat applied. This charge separation process produces positively- and negatively-charged particles with widely different efficiencies depending on the compound and its physical state. Circumstantial evidence is presented suggesting that in the new ionization process, charged particles carry analyte into the gas phase, and desolvation of these particles produce the bare ions similar to electrospray ionization, except that solid particles appear likely to be involved. This mechanistic proposition is in agreement with previous theoretical work related to ion emission from ice. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2020

Non-Equilibrium Processes, Stability, Design and Control of Pulsed Plasmas for Materials Processing

The energy and angle of impact of ions, interspersed with fluxes of neutral radicals, on the surface of wafers during plasma materials processing are critical parameters for the fabrication of microelectronic devices, nanostructure materials and biocompatible materials. This sensitivity extends to nearly all applications of plasmas to materials processing and surface modification. Low temperature plasmas for plasma materials processing have traditionally used continuously excited plasmas. However, pulsed plasma processing is an enabling technology for achieving finer resolution of microelectronics fabrication and for producing unique surface properties in a wide range of applications. This research project is addressing fundamental plasma physics issues in materials processing – the control of pulsed plasmas and the means for optimizing plasma produced reactant fluxes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Impact of Fractionation Process on the Technical and Economic Viability of Corn Dry Grind Ethanol Process

Use of corn fractionation techniques in dry grind process increases the number of coproducts, enhances their quality and value, generates feedstock for cellulosic ethanol production and potentially increases profitability of the dry grind process. The aim of this study is to develop process simulation models for eight different wet and dry corn fractionation techniques recovering germ, pericarp fiber and/or endosperm fiber, and evaluate their techno-economic feasibility at the commercial scale. Ethanol yields for plants processing 1113.11 MT corn/day were 37.2 to 40 million gal for wet fractionation and 37.3 to 31.3 million gal for dry fractionation, compared to 40.2 million gal for conventional dry grind process.

09 BIOMASS FUELS↗
DOE OSTI2021

Process Intensification and Scale-Up of a Continuous Enzymatic Hydrolysis and Separation Process

Combining separate unit operations into one where the best of each part can be maximized is one of the benefits of process intensification. An example is the combination of lignocellulosic biomass enzymatic hydrolysis with the downstream solid-liquid separation step to produce clarified sugars ready for fermentation or catalytic upgrading. The productivity and endpoint yield of enzymatic hydrolysis both enjoy the benefits of reduced feedback inhibition through the continuous removal of sugars by incorporating separations into the reactor. Likewise, the efficiency of recovering clarified sugars from the enzymatic hydrolysis slurry can be enhanced by operating separations equipment at steady-state conditions simultaneously with the continuously fed hydrolysis process. A key parameter that enables greater processing capacity while also raising the risks of failure is the solids loading or concentration. Higher solids loading allows for smaller reactor vessels and results in clarified sugars of higher concentration; however, required pumping power increases, reactor agitation may become ineffective, and membrane flux suffers. Feedstock material attributes influenced by upstream pretreatment must also be scrutinized more carefully: dilute-acid pretreated and deacetylated-and-disc-refined feedstocks exhibit different characteristics that affect agitation and pumping. The authors invite you to further explore the process science enabling the scale-up of this technology from conceptual work at the bench to pilot-scale industrially-relevant equipment where the challenges and solutions of integration and process optimization are expounded upon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Computer-aided process intensification of natural gas to methanol process

The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional methane-to-methanol process is energy intensive which negatively affects the profitability and sustainability. Here an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block-based representation and optimization methodology. The new process configuration utilizes membrane-assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO 2 -equivalent greenhouse gas emission. Such drastic improvement highlights the advantages of building block-based computer-aided process intensification method.

42 ENGINEERING↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2025

Comprehensive Analysis of the Relative Dispersion of Droplet-Size Distributions and Their Relationships to Key Physical Fog Processes Under Different Aerosol Conditions and Evolutionary Stages

The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects, radiative transfer, and microphysical processes. However, previous studies have been mostly concerned with clouds, with limited studies on fog, particularly those that examine the combined influences of all key physical processes and their roles during fog evolution. As such, this study aims to conduct a comprehensive investigation by examining the relationships between relative dispersion and other microphysical variables, as well as the underlying microphysical and dynamic processes, based on field fog campaigns in polluted and clean conditions. In polluted fog, droplet concentrations are higher, leading to smaller droplets and increased dispersion. The correlation between dispersion and droplet volume-mean radius is positive in the polluted fog, but shifts to negative in clean fog. Here, we attribute the difference to various microphysical processes like aerosol activation, condensation, collision-coalescence, and entrainment-mixing. In polluted fog, high aerosol concentrations, low supersaturations, and strong turbulence (entrainment-mixing) provide suitable conditions for the simultaneous occurrence of droplet condensation and aerosol activation, resulting in a positive correlation between dispersion and volume-mean radius, especially during the fog formation stage. In contrast, during the mature stage in clean fog, condensation is dominant with weak aerosol activation leading to a negative correlation between relative dispersion and volume-mean radius. The collision-coalescence process is more active in the mature stage, increasing radii and leading to the negative correlation between dispersion and volume-mean radius. This result sheds new light on understanding the relative dispersion and mechanisms in fog under different aerosol backgrounds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Microstructure Evolution and Mechanical Property of Low-Alloy Steel Used for Armor Layer of Flexible Pipe During Thermomechanical Process and Hot Rolling Process

In this paper, the thermomechanical process and hot rolling process were carried out to study the microstructure evolution and mechanical property of low-alloy steel used for armor layer of flexible pipes by analyzing continuous cooling transformation curve, microstructure morphology, grain characteristics, and strength. The experimental results indicate that deformation process promotes phase transformation process of both ferrite and bainite and induces the formation of fine grain. The density of low-angle grain boundary rises with increasing cooling rate. The dislocation concentration in grain becomes larger with the extended cooling rate. The microstructure of hot-rolled specimen consists of polygonal ferrite and granular bainite, which is consistent with one of the deformed specimens with similar cooling rate in dilatometer. The microstructure evolution provides effective data fundament for hot rolling process.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques

Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions.

36 MATERIALS SCIENCE↗