Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

System and method for in-process inspection within advanced manufacturing processes

A system and method can include a laser Doppler vibrometer (LDV) in optical communication with a part during manufacturing and a transducer in ultrasonic communication with the part during manufacturing. The system can also include a controller connected to both the LDV and the transducer. The controller may be configured to cause the transducer to vibrate the part during manufacturing at a predetermined frequency and the LDV may be configured to measure one or more mechanical response types of the part during manufacturing based on one or more optical characteristics of a reflected beam. The controller may further be configured to determine whether a defect is present in the part during manufacturing in response to the one or more mechanical response types of the part.

42 ENGINEERING↗
DOE OSTI2022

Active magnetic regenerative processes and systems employing hydrogen as heat transfer fluid and process gas

A system including:an active magnetic regenerative refrigerator apparatus that includes a high magnetic field section in which a hydrogen heat transfer fluid can flow from a cold side to a hot side through at least one magnetized bed of at least one magnetic refrigerant, and a low magnetic field or demagnetized section in which the hydrogen heat transfer fluid can flow from a hot side to a cold side through the demagnetized bed;a first conduit fluidly coupled between the cold side of the low magnetic field or demagnetized section and the cold side of the high magnetic field section; anda second conduit fluid coupled to the first conduit, an expander and at least one liquefied hydrogen storage module.

Holladay, Jamie D.↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

In-situ X-Ray Analysis of Rapid Thermal Processing for Thin-Film Solar Cells: Closing the Gap between Production and Laboratory Efficiency

For materials synthesis, it is well known that the material final state may not reach equilibrium and depends on the synthetic process. In particular, processes that quickly remove the available energy from the material may leave it in a metastable state and the metastability may actually impart desirable functional properties. By its very nature, Rapid thermal processing (RTP) is ideally suited to produce such metastable materials. However, metastability and the dynamics of reaching a metastable state are poorly understood, since this is best accomplished through in situ monitoring. In this regard, RTP is particularly challenging as the processing time are very short (seconds to minutes). As a result, there is only poor understanding, and hence use, of RTP in industry. This is potentially a cost-increasing limitation, because RTP can decrease cost by decreasing processing time, and as such, increase throughput and decrease the total thermal budget of processing - a significant cost. RTP is already being used for key processing steps in PV technologies. With silicon wafer PV, it is used for establishing electrical contact between the Ag metal grid and the silicon (known as firing). In this process, a silicon wafer with deposited metal/frit in a grid pattern is heated rapidly to temperatures between 750 and 800 ºC. The processing time when the temperature is held above 600ºC is short (<5 seconds). This process has historically been optimized empirically and it is unclear how the thermal processing affects formation of the final contact between the metal and the silicon. In the case of thin-film PV, RTP has been demonstrated in the process of making absorber layers, i.e. CIGS and CZTS. Use of RTP can reduce the processing time from 10s of minutes to seconds, reducing the thermal budget and increasing the throughput significantly. The conversion from precursor material to final PV material is not well understood, and most of the process optimization is done empirically. Again, the proposed in situ monitoring will provide insight into the conversion process. We have developed a unique instrument for in situ monitoring of the reaction process during RTP. The most fundamental property to monitor is the atomic arrangement (for solids, crystalline structure) and thus we have focused on this property. The chamber was designed to collect X-ray diffraction (XRD) for crystal phase determination, although it has lent insight in the formation of liquids. We designed and constructed both inert (air) and reactive (S/Se) versions of this chamber. We have applied the RTP-XRD methodology to: silicon solar cell electrical contact formation; the reaction sequence for forming CIGS PV thin films from copper selenide and indium gallium selenide precursors; the phase sequence for forming HC(NH2)2PbI3 (FAPbI3) and CH3NH3PbI3 (MAPbI3) hybrid-perovskite PV thin films. The RTP instrument enables real-time collection of X-ray diffraction data with intervals as short as 100 ms, and while heating at ramp rates as high 100 ºCs-1 up to 1200 ºC. The system is portable and can be installed on a synchrotron beamline. The RTP reactor consists of a metallic body (front cap assembly, central block and end cap assembly) that houses a quartz reaction chamber. The sample is secured inside the reaction chamber on a sample holder connected to the front-cap assembly. Modifications of the chamber were made for safely handling sulfur and selenium vapor environments. A Se/S source holder was added to generate a Se/S over pressure in the chamber on heating and the entrance window is made wider in order to accommodate the detachable additional holder. Se/S are toxic in nature and therefore a proper Se/S trapping is used before releasing the gases to exhaust. Screen-printing provides an economically attractive means for making Ag electrical contacts to Si solar cells, but the use of Ag substantiates a significant manufacturing cost, and the glass frit used in the paste to enable contact formation contains Pb. To achieve optimal electrical performance and to develop pastes with alternative, abundant, and non-toxic materials requires understanding the contact formation process during firing. We use the RTP-XRD chamber during firing of Ag-Si contacts to reveal the reaction sequence for contact formation. The findings show that between 500 ºC and 650 ºC PbO in the frit etches the SiNx antireflective-coating on the solar cell, exposing the Si surface. Then, above 650 ºC, Ag+ dissolves into the molten glass frit – key for enabling deposition of metallic Ag on the emitter surface and precipitation of Ag nanocrystals within the glass. This clarifies contact formation mechanisms and can be used to suggest approaches for development of inexpensive, nontoxic solar cell contacting pastes. We used the RTP/XRD chamber to study the formation of CuInGaS/Se (CIGS) PV films by in-situ selenization of pre-deposited metal stacks and annealing of pre-deposited Cu-In-Ga-Se precursors. During the metal stack selenization, we observed that Se first reacts with the metals and forms selenides. We observed formation of intermetallics such as Cu2In and binary selenides during heating. On further annealing in a Se atmosphere, CuInSe2 and CuGaSe2 forms which on heating up to 550 °C reacts further to form the CIGS phase. On prolonged exposure to 550 °C for more than 5 mins or higher temperature, we see separation into Ga-rich and Ga-depleted CIGS phases. On introducing a holding step at 400 °C for 10 min during the annealing ramp, the CIGS phase (forms between 500-550 °C), appears to be less prone to decomposition into high-Ga and low-Ga phases during holding at peak temperature. Lead halide perovskites have emerged as successful optoelectronic materials with high photovoltaic power conversion efficiencies and low material cost. However, substantial challenges remain in the scalability, stability and fundamental understanding of the materials. We demonstrated the application of radiative thermal annealing, an easily scalable processing method, for synthesizing formamidinium lead iodide (FAPbI3) perovskite solar absorbers. Devices fabricated from films formed via radiative thermal annealing have equivalent efficiencies to those annealed using a conventional hotplate. By coupling results from in situ x-ray diffraction using a radiative thermal annealing system with device performances, we mapped the processing phase space of FAPbI3 and corresponding device efficiencies. Our map of processing-structure-performance space suggests the commonly used FAPbI3 annealing time, 10 min at 170 °C, can be significantly reduced to 40 s at 170 °C without affecting the photovoltaic performance. Applying the Johnson-Mehl-Avrami model to the decomposition into PbI2, the activation energy was determined. We also presented a well-controlled, manufacturing relevant annealing method, radiative thermal annealing, for the methylammonium lead triiodide films formed by solvent engineering process, with dimethylformamide (DMF) and dimethyl sulfoxide as solvent and diethyl ether as the antisolvent. Radiative thermal annealing can produce high quality perovskite films, evidenced by high efficiency solar cell devices, in a shorter time than the widely-used hotplate annealing. Using in-situ x-ray diffraction during the radiative annealing, we show that the role of the antisolvent is not to form an intermediate compound (a methylammonium iodide, lead iodide, and dimethyl sulfoxide compound) by washing of the main solvent (DMF) but to achieve a pin-hole free, uniform film of methylammonium lead iodide. We find that having the intermediate compound does not guarantee a high quality (pin-hole free) perovskite film. Our study was extended to reveal the effect of annealing environment, annealing temperature profile, and deposition substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗