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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of Fe-6Si Soft Magnetic Alloy Produced by Laser-Directed Energy Deposition Additive Manufacturing

Abstract Commercial electrical steels, Fe-Si alloys with < 4 wt.% Si, are inexpensive and efficient materials for electrical power conversion. Further efficiency improvements require increasing the silicon concentration to 6 wt.%, at which point the material becomes brittle and difficult to form by conventional rolling and sheet fabrication methods. Additive manufacturing stands to overcome challenges with commercial manufacturing techniques by leveraging near-net-shape fabrication. The wide array of process conditions provides additive manufacturing with increased flexibility, enabling control over the microstructure and mechanical properties. This work explores the microstructures and magnetic properties of ring-shaped Fe-Si alloys produced using concentric and cross-hatch tool paths on a laser-directed energy deposition additive manufacturing system. Concentric-built samples exhibit elongated grain structures while cross-hatch-built samples comprise lower aspect ratio grain structures. Thermal finite element analysis simulations model the stress conditions produced by the different scan path geometries. Microhardness measurements probe the mechanical properties as a function of anneal temperature, providing a qualitative understanding of the intergranular defect density. Soft magnetic properties measured under quasistatic and AC conditions show frequency- and microstructure-dependent coercivity and permeability. Finally, analysis of the core loss quantifies how the build strategies and thermal treatments influence efficiency in electrical power conversion applications. Understanding the influences of scan path geometry and thermal treatment provides a pathway towards application of additively manufactured soft magnetic materials.

Adamczyk, Jesse M.↗
DOE OSTI2024

Standardization Gaps in Powder Feedstock Characterization and Establishing Acceptability for Reuse in Additive Manufacturing

Characterization techniques for powder feedstocks used in additive manufacturing (AM) have long been relied upon to describe the inputs to an AM workflow. However, functional gaps remain between tests to measure intrinsic and extrinsic properties with the direct performance within AM equipment. Furthermore, the common practice of reusing powder through multiple build cycles introduces effects and changes to feedstock performance that are otherwise difficult to measure quantitatively. Here, standardization and the development of new test methods have not kept pace with the rapid evolution of the AM industry and its reliance on highly coupled process-structure–property-performance relationships.

Characterization and Analytical Technique↗
DOE OSTI2025

Chloride Molten Salt Electrolysis Enables Integrated and Energy-Efficient Process for NdFeB Magnet Fabrication

Rare-earth elements (REEs) have been identified by NATO, the USDOE, and USGS as critical materials, i.e., materials which have significant demand yet pose supply-chain risks. Many of the existing processes for separations, metallization, and final parts production used across the REE supply chain involve energy intensive steps. For example, neodymium (Nd or NdPr) is produced using oxyfluoride electrolysis of Nd 2 O 3 , which requires hydrofluoric acid to produce a key electrolyte component (NdF 3 ) and generates undesired perfluorocarbon (PFC) gases. Such challenges make securing a resilient supply chain for NdFeB permanent magnets in countries like the United States prohibitively difficult. Here, we propose a chloride-based MSE process that circumvents these challenges, delivering high-purity NdPr from a (NdPr)Cl 3 feed from upstream REE separations. This eliminates environmentally-damaging steps of oxalate or carbonate precipitation and calcination, and enables superior production rates due to greater solubility of (NdPr)Cl 3 in chloride melts compared to Nd 2 O 3 . We show that CMSE generates high-purity NdPr (99.4 wt.%) while being energy-efficient (~ 6 kWh/kg-Nd). NdPr from CMSE was used to fabricate a NdFeB magnet with an excellent maximum energy product (> 40 MGOe), comparable to commercially available NdFeB magnets. This establishes CMSE as a leading approach for integrated, energy-efficient NdFeB magnet production.

Materials science↗
DOE OSTI2019

Cancer Treatment-Associated Pericardial Disease: Epidemiology, Clinical Presentation, Diagnosis, and Management

Purpose of Review: Cancer therapeutics have seen tremendous growth in the last decade and have been effective in the treatment of several cancer types. However, with advanced therapies like kinase inhibitors and immunotherapies, there have been unintended consequences of cardiotoxicities. While traditional chemotherapy and radiation-induced cardiotoxicity have been well studied, further research is needed to understand the adverse effects of newer regimens. Recent Findings: Both immune-mediated and non-immune-medicated cytotoxicity have been noted with targeted therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitors. In this manuscript, we describe the pericardial syndromes associated with cancer therapies and propose management strategies. Pericardial effusion and pericarditis are common presentations in cancer patients and often difficult to diagnose. Concomitant myocarditis may also present with pericardial toxicity, especially with immunotherapies. In addition to proper history and physical, additional testing such as cardiovascular imaging and tissue histology need to be obtained as appropriate. Holding the offending oncology drug, and institution of anti-inflammatory medications, and immunosuppressants such as steroids are indicated. Summary: A high index of suspicion, use of standardized definitions, and comprehensive evaluation are needed for early identification, appropriate treatment, and better outcomes for patients with cancer treatment-associated pericardial disease. Further research is needed to understand the pathophysiology and to evaluate how the management of pericardial conditions in these patients differ from traditional management and also evaluate new therapies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Small cell carcinoma of the bladder: the characteristics of molecular alterations, treatment, and follow-up

Small cell carcinoma of the bladder (SCCB) is a rare disease associated with high invasiveness and mortality. Histologically, SCCB is difficult to distinguish from small cell lung cancer (SCLC); however, it shares more similar molecular alterations with urothelial carcinoma (UC). As a result, now, the widely accepted theory about the cells of origin is that SCCB and UC probably have a common clone origin. Even the former probably comes from a preexisting UC. At present, given its rarity, early diagnoses, treatments, and follow-ups are not well established, which are vital to patients with SCCB. Inspirationally, in recent years, with the development of molecular diagnostic methods, molecular alterations of SCCB have been understood partially, which are propitious to excavate new potential therapeutic strategies and establish sound follow-ups. Therefore, the future will be light for patients with SCCB.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Generation and representation of synthetic smart meter data

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Structure-aware methods for expensive derivative-free nonsmooth composite optimization

We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (MS-P) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (GOOMBAH) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at https://github.com/POptUS/ IBCDFO/.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Derivation of heterogeneous material distributions and their sensitivity to HM-coupled two-phase flow models exemplified with the LASGIT experiment

Abstract Advective gas transport in bentonite, a possible buffer material in repositories for radioactive materials, is difficult to simulate in numerical continuum models, partly due to the complicated microstructure of bentonite. To generate reliable models of repositories nevertheless, spatially distributed heterogeneous material properties can be used to allow localization of gas flow. In this study, a pore-size-dependent stochastic approach of the gas entry pressure is derived from Mercury Intrusion Porosimetry, which is used to replicate measurements from the LASGIT experiment. In addition, three benchmark tests are simulated to investigate the dependence of heterogeneous distributions of material properties on the mesh discretization, the temporal dependence, and the coupling between the processes influenced by the heterogeneous parameters. The numerical modeling results of the LASGIT experiment show that the onset of gas flow into the system and the subsequent increase in pressure and stress can be well reproduced using heterogeneous distributions. Compared to a model with homogeneous material properties, heterogeneous distributions may allow the generation of dilatancy-controlled microfractures—an important feature with regard to the advective gas flow in bentonites. However, it can be observed that the heterogeneous distributions in LASGIT are less significant, as technical gaps or differences in material types could have a greater impact.

Environmental Sciences & Ecology↗
DOE OSTI2024

Reductive catalytic fractionation of cotton stalks: catalytic strategy for tuning the selectivity of phenolic monomers

Pretreatment of lignocellulosic biomass is a primary step to delink lignin from the lignin-carbohydrate complex for bioethanol production and other value-added products. Presently, these processes yield lignin (technical) with significant structural changes compared to native lignin and make it difficult to valorize. Reductive catalytic fractionation (RCF) is an advanced pretreatment process to valorize native lignin to selective phenolic monomers before capitalizing on carbohydrates. Herein, cotton stalks (CS) were pretreated with ethanol: water mixture catalyzed by bimetallic, Ru-Ni/HY (RNY) and Ru-Fe/HY (RFY), and trimetallic, Ru-Fe-Ni/HY (RFNY), catalysts using the endogenously produced H 2 . The favorable catalytic properties of the RFNY catalyst, including high reducibility, acidity, metal dispersion, the synergistic effect of all the metals, and appropriate pore and particle size resulted in the highest catalytic activity. Maximum 92% delignification efficiency was achieved with 91% holocellulose retention. The extracted lignin after depolymerization in the same step produced a 19 wt.% yield of phenolic monomers. Increasing the duration of the reaction from 2 to 6 h increased the selectivity to saturated alkyl chain phenolics (1 to 16 wt.%) at the expense of hemicellulose (50 to 9%). The fate of carbohydrates left behind after the RCF (CS-RCF) process was analyzed through enzymatic saccharification and compared with raw CS and acid-pretreated CS. In conclusion, CS-RCF yielded maximum glucose yield (45 g/L) within an incubation period of 72 h.

09 BIOMASS FUELS↗
DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Validation Data for Benchmarking Wire Arc Additive Manufacturing Process Simulations

Residual stresses cause geometric distortion and affect mechanical performance of additively manufactured structures, yet they are notoriously difficult to assess and predict. Distortion (warpage) can drive parts outside dimensional tolerance limits, leading to part rejection or rework. For parts that meet tolerance, locked-in residual stress fields can affect structural integrity during operation, particularly subcritical cracking by fatigue, creep, or corrosion. This work develops benchmark data for a common additive manufacturing process (Wire Arc Additive Manufacturing) that can be applied for calibration and validation of physical process models that predict residual stress fields. The work includes design of two different samples of differing geometry, detailed manufacturing records for a set of physical samples, and an extensive set of residual stress measurement data developed using two diverse techniques (the contour method and neutron diffraction). An initial application of the work is also reported, where a modeling challenge was issued to secure residual stress model predictions from two independent laboratories that were blind to residual stress measurement data. These initial blind residual stress predictions show significant discrepancies relative to the measurement data, illustrating the potential value of the underlying validation data. An open repository for this work, including the sample designs, manufacturing process records, and the residual stress data, is also provided for future application in non-blind validation efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Meshfree simulation and experimental validation of extreme thermomechanical conditions in friction stir extrusion

Abstract Friction stir extrusion (FSE) is a novel solid-phase processing technique that consolidates and extrudes metal powders, flakes, chips, or billets into high-performance parts by plastic deformation, which has the potential to save substantial processing time and energy. Currently, most studies on FSE are experimental and only a few numerical models have been developed to explain and predict the complex physics of the process. In this work, a meshfree simulation framework based on smoothed particle hydrodynamics (SPH) was developed for FSE. Unlike traditional grid-based methods, SPH is a Lagrangian particle-based method that can handle severe material deformations, capture moving interfaces and surfaces, and monitor the field variable histories explicitly without complicated tracking schemes. These aspects of SPH make it attractive for the FSE process, where in situ evolution of field variables is difficult to observe experimentally. To this end, a 3-D, fully thermomechanically coupled SPH model was developed to simulate the FSE of aluminum wires. The developed model was thoroughly validated by comparing the numerically predicted material flow, strain, temperature history, and extrusion force with experimental results for a certain set of process parameters. The validated SPH model can serve as an effective tool to predict and better understand the extreme thermomechanical conditions during the FSE process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

New electric force and charge exchange modules in discrete element model enables particle dynamics simulation in electric field

Discrete element modeling (DEM) is an important technique for particle dynamics simulation. The field of metal additive manufacturing often utilizes DEM to simulate the rheological behaviors of powder. Standard contact and short-range interactions are sufficient in most cases but insufficient to describe the particle dynamics with the influence of an electric field. Modeling such a system requires additional physics to describe the particle–field interactions. The relevant physics has been experimentally understood but is not yet available in DEM. Here, we develop a charge exchange and an electric force module. The electric force module governs particle response to the electric field, while the charge exchange module enables particles to acquire proper charge during contact with charged geometries. We validate the modules against analytical calculations and high-speed videos of electrostatic powder deposition experiments. Notably, the model struggles to capture the initial particle levitation. We later deploy a modified electric field, as supported by static electric field simulation, to better approximate the electric field penetration into the powder layer. This modification improves the model’s capability of simulating realistic particle levitation. The results highlight the challenges of modeling particle behaviors in the electric field while demonstrating the feasibility of obtaining quantitative results, which are difficult to measure experimentally.

charge exchange↗
DOE OSTI2020

Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6

The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermodynamic Modeling of Solid Flux Interactions with Molten Aluminum

Abstract Oxide and dross formation during aluminum melt processing results in a considerable amount of loss of metallic aluminum. The total melt loss generated during melt processing can be greatly reduced by efficient use of flux, particularly for melting aluminum scrap or secondary alloys. Effective use of cover fluxes can significantly reduce dross generation by creating a barrier between the metallic aluminum and the atmosphere. The amount of metallic aluminum trapped within the dross layer can also be reduced by up to 50% using drossing fluxes. However, reducing the industrial average melt loss has remained difficult. To evaluate flux ingredients used in the casting industry, computational thermodynamic software ThermoCalc was used to calculate the driving force for reactions between twenty-one flux ingredients and eighteen common alloying and impurity elements in foundry alloys. The thermodynamic calculations, combined with other properties, were used to provide a desirable list of cover and drossing ingredients, which are being experimentally validated.

Moodispaw, Michael P. (ORCID:0000000267635882)↗
DOE OSTI2021

Powder spreading, densification, and part deformation in binder jetting additive manufacturing

Binder jetting additive manufacturing (AM) can print complex structures in economical and scalable manner. Binder jetting AM comprises of deposition and weak binding of particles, known as green part, at room temperature and subsequent binder removal and sintering densification at high temperatures. However, during the densification (i.e., sintering), the part significantly deforms due to volume shrinkage. The deformation during sintering is difficult to predict, which prevents the widespread application of this technology. In this research, powder spreading simulation using discrete element method (DEM) was performed first to capture local variations in powder bed configuration. Second, finite element method (FEM) with a phenomenological continuum constitutive model was used to predict part shrinkage during the sintering process. DEM simulation showed variations in packing density, particle segregation, formation of uneven powder bed surface, and shift in particle size distribution (PSD). The sintering simulation modeled part deformation with a reasonable accuracy of 3% for solid-state sintering and intermediate liquid phase sintering. A demonstration case with non-uniform initial packing density showed that inhomogeneous green part density and PSD should be accounted for prediction of part deformation in binder jetting AM.

36 MATERIALS SCIENCE↗