Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “difficult”

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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Synchrotron experiment and simulation studies of magnesium-steel interface manufactured by impact welding

The effective weight reduction in the automotive industry by the wide adoption of lightweight magnesium (Mg) alloys demands high-quality joint between magnesium alloys and massively-used steels in order to wring the excess weight with strength and safety assurance. However, Mg-steel joint is difficult to achieve because there is no mutual solubility between magnesium and steel and huge disparity in physical properties. An impact-based welding method recently showed successful Mg-steel joining. In this work, the characteristics of Mg-steel interface joined by the impact welding method were investigated. Synchrotron high-energy X-ray computed tomography and diffraction were applied to characterize the microstructure across Mg-steel interface. Results revealed a deposit layer formed at the joint interface where Fe-rich particles spread deep into the Mg matrix. High-resolution 3D morphology of Mg-steel interface demonstrated the trapped pores and cracks inside the deposit layer. Finally, the formation of the deposit layer and the void/cracking evolution were analyzed by using finite element models. These findings provide insights into the immiscible Mg-steel joining process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of Re and Al additions on the microstructure and mechanical properties of Nb-18Ti-12W alloy

Microstructure and mechanical properties at 25–1200 °C of new refractory alloys, Nb-18Ti-12W (NTW), Nb-18Ti-12W-5Re (NTW-R) and Nb-18Ti-12W-5Re-1Al (NTW-RA), all compositions are in atomic percent, are reported. The alloys are developed as an alternative to a denser, more expensive and difficult-to-process refractory alloy WC-3009. After production by arc melting and hot isostatic pressing at 1400 °C, 207 MPa for 3 h, the alloys have a BCC crystal structure with the average grain size of ~150–200 μm and contain low volume fraction of (Ti,N)-rich precipitates near grain boundaries. NTW shows excellent malleability at all studied temperatures. Its yield stress at 25 °C, 1000 °C and 1200 °C is 874 MPa, 353 MPa and 202 MPa, respectively. The NTW-R and NTW-RA alloys are stronger at all the studied temperatures; however, they lack in compression ductility at 25 °C. As a result, extensive deformation twinning and formation of intergranular voids precede the fracture of these two alloys at 25 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rechargeable alkaline zinc–manganese oxide batteries for grid storage: Mechanisms, challenges and developments

Rechargeable alkaline Zn–MnO 2 (RAM) batteries are a promising candidate for grid-scale energy storage owing to their high theoretical energy density rivaling lithium-ion systems (~400 Wh/L), relatively safe aqueous electrolyte, established supply chain, and projected costs below $100/kWh at scale. In practice, however, many fundamental chemical and physical processes at both electrodes make it difficult to achieve commercially competitive energy density and cycle life. In this paper, we present a detailed and timely analysis of the constituent materials, current commercial status, electrode processes, and performance-limiting factors of RAM batteries. We also examine recently reported strategies in RAM and related systems to address these issues through additives and modifications to the electrode materials and electrolyte, special ion-selective separators and/or coatings, and unconventional cycling protocols. We conclude with a critical summary of these developments and discussion of how future studies should be focused toward the goal of energy-dense, scalable, and cost-effective RAM systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Towards additive manufacturing of magnetocaloric working materials

The feasibility of three-dimensional printing via the Directed Energy Deposition (DED) technique is demonstrated for the ferromagnetic AlFe 2 B 2 (aka 1-2-2) compound, which shows promise as a working material for room-temperature magnetocaloric devices. Like many intermetallic materials, the 1-2-2 compound is brittle at room temperature; not only is it difficult to synthesize from the melt, it is challenging to form it into useful geometries necessary for effective heat transfer in magnetocaloric devices. Using Laser Engineered Net Shaping (LENS TM ) synthesis from two powder precursors, AlB 2 and Fe, samples of simple (rod) and complex (honeycomb) architectures were successfully synthesized. Furthermore, the phase constitution and distribution, magnetic transition temperature, and saturation magnetization determined for the LENS TM -synthesized rod sample are all similar to those obtained for 1-2-2 materials made by conventional casting. Further, the measured magnetic entropy change, as determined from Maxwell's Relations applied to magnetic data, indicates that the magnetocaloric response of the annealed LENS TM -printed rod samples are comparable to those of the 1-2-2 compounds made by conventional techniques. These results confirm that complex magnetofunctional shapes, which are needed for efficient energy conversion in magnetocaloric devices, can be rapidly fabricated from the AlFe 2 B 2 compound.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Decoupling of strain and temperature effects on microstructural evolution during high shear strain deformation

The interplay between defect generation by shear strain and defect annihilation by local heating is difficult to predict in shear-assisted processing techniques. In this study, we decoupled the effects of high shear strain and external heating in an immiscible Cu-Nb alloy using a pin-on-disk tribometer to mimic the microstructural evolution of material during solid-phase processing. The change in sub-surface deformation, strain distribution, and redistribution of the second phase as a function of temperature were examined using transmission electron microscopy and atom probe tomography. Zener-Hollomon parameter is used to semi-quantify the deformation of Cu-Nb alloys as a function of strain and temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Amorphous complexion-aided sintering enables scalable processing of bulk nanocrystalline Cu-Zr with high strength and compressive plasticity

Nanocrystalline alloys can have exceptional strengths, yet due to limited microstructural stability it is difficult to fabricate bulk pieces through traditional processing routes that retain nanosized grains. In this study, centimeter-sized Cu-Zr alloy pellets were fabricated via a simple and improved powder metallurgy processing route. Different consolidation temperatures and times were employed to investigate the effect of amorphous grain boundary complexions on densification and the resulting mechanical properties. Bulk compression tests were carried out, with the samples that were hot pressed at 900 °C for 10 h exhibiting an excellent combination of average yield strength of 722 ± 45 MPa and average failure strain of 25.3 ± 2.4 %. Furthermore, we find that a powder processing route which enables amorphous complexion-assisted sintering leads to samples that (1) reach full density without requiring quenching treatments or other complex processing, (2) demonstrate appreciable plasticity, and (3) have strength that competes with commercially available high-strength Cu alloys.

Bulk nanocrystalline alloy↗
DOE OSTI2021

The nanoscale distribution of copper and its influence on charge collection in CdTe solar cells

For decades, Cu has been the primary dopant in CdTe photovoltaic absorbers. Typically, Cu acceptor concentrations in these devices are on the order of 1 1014 cm-3, which has made it notoriously difficult to directly correlate nanoscale Cu distributions to the local charge transport properties of these devices. To measure and correlate these properties, measurement techniques require high sensitivity to elemental concentration, large penetration depth, and operando compatibility. Techniques such as secondary-ion mass spectroscopy and X-ray energy dispersive spectroscopy are widely adopted to measure Cu concentrations, but they are limited by penetration depth, sensitivity, or spatial resolution. Additionally, they lack the operando capabilities required to correlate one-to-one Cu concentrations to electrical performance. In this work, correlative X-ray microscopy is used to investigate the spatial distribution of Cu and its impact on charge collection through the depth and breadth of CdTe photovoltaic devices. Plan-view, nanoscale X-ray fluorescence maps clearly demonstrate the spatial segregation of copper around regions thought to be CdTe grain boundaries. Complementary cross-section imaging unveils the transition of the maximum charge-collection efficiency from the ZnTe-CdTe interface to the CdS-CdTe interface as a function of Cu incorporation. The copper concentration through the depth of the CdTe layer is characterized by slow and fast diffusion components, and cross-section charge-transport modeling shows that the experimentally obtained charge collection can be explained by the modeled acceptor distribution through the depth of the CdTe layer.

14 SOLAR ENERGY↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Characterization of nonlinear ultrasonic waves behavior while interacting with poor interlayer bonds in large-scale additive manufactured materials

Over the past decades, researchers have developed several nonlinear ultrasonic techniques for quality control of materials commonly used in different applications. Owing to the superior sensitivity of nonlinear ultrasound waves to small defects such as micro-cracks, their applicability in different nondestructive testing (NDT) problems has been investigated in numerous studies. These studies utilize frequency domain analysis to detect the generation of higher harmonics because of the formation of defects in the inspected medium. Frequency domain analysis based on the Fourier transform is a significant approach used in linear systems; however, it may not perform adequately on nonlinear systems. Hence, studies on nonlinear dynamics and physics consider analyzing systems' behavior in the phase-space domain. In contrast to the frequency domain analysis, which can result in information loss, analysis in the phase-space domain retains all the information regarding a system's states. Here, we investigate the nonlinearities induced by poor interlayer bonding in polymer-based additive manufactured parts in the phase-space domain. It is convenient to characterize the nonlinearity in the phase-space domain because it provides a geometrical representation of a system's states. Two types of low quality interlayer bond are considered. The first type is simulated artificially while the second type is manufactured by reducing the bond quality during the printing process. The analysis verified that the received ultrasonic signals exhibit classical nonlinear behavior in the phase-space domain while interacting with simulated poor interlayer bonds. In addition, the results showed that the behavior of ultrasonic waves is amplitude-dependent and evolves into models that have not been previously reported. Furthermore, Largest Lyapunov Exponent (LLE) is used to quantify the behavior of nonlinear ultrasonic waves while interacting with poor interlayer bonds. Using LLE, it was observed that the divergence rate of the phase-space trajectories depends on the amplitude of the excitation. This observation quantitatively proves that nonlinear behavior of ultrasound while interacting with poor interlayer bonds can be amplitude-dependent. The results of both simulated and inherent poor interlayer bond cases showed that LLE can be used as a reliable quantitative damage-sensitive feature to detect and potentially characterize weak bonds, which are difficult to detect using conventional approaches. Additionally, the reported results in the phase-space domain provide a basis for proposing a new mathematical model for ultrasonic waves interacting with poor interlayer bonds.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation of thermal hydraulic behavior of the High Temperature Test Facility's lower plenum via large eddy simulation

A high-fidelity computational fluid dynamics (CFD) analysis was performed using the Large Eddy Simulation (LES) model for the lower plenum of the High–Temperature Test Facility (HTTF), a ¼ scale test facility of the modular high temperature gas-cooled reactor (MHTGR) managed by Oregon State University. In most next–generation nuclear reactors, thermal stress due to thermal striping is one of the risks to be curiously considered. This is also true for HTGRs, especially since the exhaust helium gas temperature is high. In order to evaluate these risks and performance, organizations in the United States led by the OECD NEA are conducting a thermal hydraulic code benchmark for HTGR, and the test facility used for this benchmark is HTTF. HTTF can perform experiments in both normal and accident situations and provide high-quality experimental data. However, it is difficult to provide sufficient data for benchmarking through experiments, and there is a problem with the reliability of CFD analysis results based on Reynolds–averaged Navier–Stokes to analyze thermal hydraulic behavior without verification. To solve this problem, high-fidelity 3-D CFD analysis was performed using the LES model for HTTF. It was also verified that the LES model can properly simulate this jet mixing phenomenon via a unit cell test that provides experimental information. As a result of CFD analysis, the lower the dependency of the sub-grid scale model, the closer to the actual analysis result. In the case of unit cell test CFD analysis and HTTF CFD analysis, the volume-averaged sub-grid scale model dependency was calculated to be 13.0% and 9.16%, respectively. As a result of HTTF analysis, quantitative data of the fluid inside the HTTF lower plenum was provided in this paper. As a result of qualitative analysis, the temperature was highest at the center of the lower plenum, while the temperature fluctuation was highest near the edge of the lower plenum wall. The power spectral density of temperature was analyzed via fast Fourier transform (FFT) for specific points on the center and side of the lower plenum. FFT results did not reveal specific frequency-dominant temperature fluctuations in the center part. It was confirmed that the temperature power spectral density (PSD) at the top increased from the center to the wake. The vortex was visualized using the well-known scalar Q-criterion, and as a result, the closer to the outlet duct, the greater the influence of the mainstream, so that the inflow jet vortex was dissipated and mixed at the top of the lower plenum. Additionally, FFT analysis was performed on the support structure near the corner of the lower plenum with large temperature fluctuations, and as a result, it was confirmed that the temperature fluctuation of the flow did not have a significant effect near the corner wall. In addition, the vortices generated from the lower plenum to the outlet duct were identified in this paper. It is considered that the quantitative and qualitative results presented in this paper will serve as reference data for the benchmark.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2019

Optimization of a superconducting adiabatic radio frequency neutron resonant spin flipper

The neutron radio frequency (RF) spin flipper is one of the key components to implement neutron resonance spin echo (NRSE) for high resolution measurements of dynamics in materials. Various types of RF flippers have been built and implemented including adiabatic and non-adiabatic RF flippers. For a pulsed neutron source, adiabatic RF flipper is more favorable due to its wide operation bandwidth. However, the optimization criteria of an adiabatic RF flipper is still lacking. Also they are difficult to be operated in bootstrap mode, which can be use to minimize the phase aberration and increase the achievable resolution. In the report by Dadisman et al. (2019), an adiabatic RF flipper has been built for the first time with superconducting technologies, which allows bootstrap mode. Here in this article, the method of optimizing the device and the detailed design will be discussed for each component of the device.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Measurements of D–D fusion neutrons generated in nanowire array laser plasma using Timepix3 detector

Here, we present results of the neutron detection in laser plasma experiment with CD2 nanowire target. A hybrid semiconductor pixel detector Timepix3 covered with neutron converters was using for the detection neutrons. D-D fusion neutrons were detected in a plastic converter through recoiled protons. Both the energy of recoiled protons and time-of-flight of neutrons (and thus their energy) were determined. We report (2.4±1.8) × 10 7 neutrons generated for 1 J of incoming laser energy. Furthermore, we proved that Timepix3 is suitable for difficult operational conditions in laser experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Deployment of a double scatter system for directional detection of background neutron radiation

The detection of special nuclear materials (SNM) requires the understanding of nuclear signatures that allow the discrimination against background. In particular, understanding neutron background characteristics such as count rates and energies and their correlations with environmental conditions and surroundings of measurement locations is important in enhancing SNM detection capabilities. The Mobile Imager of Neutrons for Emergency Responders (MINER) was deployed for 8 weeks in downtown San Francisco (CA) to study such neutron background characteristics in an urban environment. Of specific interest was the investigation of the impact of surrounding buildings on the neutron background count rates and to answer the question whether buildings act as absorber of neutrons or as sources via the so-called ship effect. MINER consists of 16 liquid scintillator detector elements and can be operated as a neutron spectrometer, as a neutron imager, or simply as a counter of fast neutrons. As expected, the neutron background rate was found to be inversely proportional to the atmospheric pressure. In the energy range where MINER is most sensitive, approximately 1–10 MeV, it was found that the shape of the detected background spectrum is similar to that of a detected fission spectrum, indicating the limited discrimination power of the neutron energy. The similarities between the detected background neutron spectrum and fission sources makes it difficult to discriminate SNM from background based solely on the energies observed. The images produced using maximum likelihood expectation maximization revealed that neutrons preferentially are coming from areas in the environment that have open sky, indicating that the surrounding buildings act as absorbers of neutrons rather than sources as expected by the ship effect. Here, the inherent properties of a neutron scatter camera limit the achievable image quality and the effective deployment to systematically map neutron background signatures due to the low count rate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Development of a versatile, high-temperature, high-throughput ion irradiation system

We report ion irradiation has long been used as a surrogate for neutron irradiation experiments which are comparatively slower, more expensive, and often can leave materials activated and difficult to handle post-irradiation. However, with the growing use of combinatorial synthesis methods and high-throughput, automated characterization techniques, ion irradiation facilities will need to upgrade their capabilities to be able to accommodate these new material platforms. To address this challenge, a high-throughput ion irradiation system has been developed at the University of Wisconsin-Madison Ion Beam Laboratory (IBL). The system is capable of accommodating large sample arrays of arbitrary geometries and heat samples individually using an infrared laser to enable high-temperature, high-throughput ion irradiation while mitigating the risk of annealing the irradiation damage in nearby samples. To control stage motion, laser power, and beam current measurement, among other tasks, the program Chronos was developed which enables automated high-throughput irradiation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Characterization of a collimated neutron imager for low-rate fast neutron imaging

Spatial localization of special nuclear materials (SNM) via their neutron signatures amidst background requires knowledge of the background neutron environment or a means of separating a source from background based on low amounts of information. This requirement has created the need for characterizing the spatial distribution of the cosmogenic neutron background. Neutron scatter cameras have been developed and optimized for rapid detection of high activity sources, but have low imaging efficiency, making it difficult to use them to characterize low rate diffuse sources, such as the neutron background. The Low Intensity Neutron Imaging System (LINIS) is a collimated neutron imager that has been designed and optimized for imaging diffuse cosmogenic neutron background in the energy range of 0.5–15 MeV. LINIS operates using 16 liquid scintillation detectors shielded by ultra-high molecular weight polyethylene cylindrical collimators in a staggered orientation and rotates to 7 discrete positions, giving it roughly 2π sensitivity. Finally, LINIS has been characterized using (α, n) and fission neutron sources using two imaging techniques for neutron source localization, simple backprojection and Maximum Likelihood Expectation Maximization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention

Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's source. Often, detection of the physical phenomenon (termed signal) is difficult to distinguish from naturally occurring phenomena (termed background). In this case, the discrimination of signal events from background can be performed using classifiers, and they may range from simple, threshold-based classifiers to sophisticated neural networks. These classifiers are often trained and validated to obtain optimal accuracy, however we show that the optimal accuracy classifier does not generally coincide with a classifier that provides the lowest detection limit, nor the lowest quantification uncertainty. Here, we present a derivation of the detection limit and quantification uncertainty in the classifier-based counting experiment case. We also present a novel abstention mechanism to minimize the detection limit or quantification uncertainty a posteriori. We illustrate the method on two data sets from the physical sciences, discriminating Ar-37 and Ar-39 radioactive decay from non-radioactive events in a gas proportional counter, and discriminating neutrons from gammas in an inorganic scintillator and report results therefrom.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗