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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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149 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Role of hypoxia-inducible factor-1 in transcriptional activation of ceruloplasmin by iron deficiency

A role of the copper protein ceruloplasmin (Cp) in iron metabolism is suggested by its ferroxidase activity and by the tissue iron overload in hereditary Cp deficiency patients. In addition, plasma Cp increases markedly in several conditions of anemia, e.g. iron deficiency, hemorrhage, renal failure, sickle cell disease, pregnancy, and inflammation. However, little is known about the cellular and molecular mechanism(s) involved. We have reported that iron chelators increase Cp mRNA expression and protein synthesis in human hepatocarcinoma HepG2 cells. Furthermore, we have shown that the increase in Cp mRNA is due to increased rate of transcription. We here report the results of new studies designed to elucidate the molecular mechanism underlying transcriptional activation of Cp by iron deficiency. The 5'-flanking region of the Cp gene was cloned from a human genomic library. A 4774-base pair segment of the Cp promoter/enhancer driving a luciferase reporter was transfected into HepG2 or Hep3B cells. Iron deficiency or hypoxia increased luciferase activity by 5-10-fold compared with untreated cells. Examination of the sequence showed three pairs of consensus hypoxia-responsive elements (HREs). Deletion and mutation analysis showed that a single HRE was necessary and sufficient for gene activation. The involvement of hypoxia-inducible factor-1 (HIF-1) was shown by gel-shift and supershift experiments that showed HIF-1alpha and HIF-1beta binding to a radiolabeled oligonucleotide containing the Cp promoter HRE. Furthermore, iron deficiency (and hypoxia) did not activate Cp gene expression in Hepa c4 hepatoma cells deficient in HIF-1beta, as shown functionally by the inactivity of a transfected Cp promoter-luciferase construct and by the failure of HIF-1 to bind the Cp HRE in nuclear extracts from these cells. These results are consistent with in vivo findings that iron deficiency increases plasma Cp and provides a molecular mechanism that may help to understand these observations.

NASA Discipline Cardiopulmonary↗
DOE OSTI2025

The MCNP ® 6 code: A decade of progress

After several years of effort involved in merging the Los Alamos National Laboratory MCNP5 and MCNPX codes, in 2013 the first production release of version 6 of the Monte Carlo N-Particle ® , or MCNP ® , code MCNP6.1 was distributed publicly. Since then, three significant releases have been issued: MCNP6.1.1beta in 2014, MCNP6.2 in 2018, and MCNP6.3 in 2023. While each release always contains new features, code enhancements, and bug fixes, each version has had a different primary focus, ranging from improved calculational efficiency to new powerful utilities and tools, to software modernization of the code base. With all that has been learned over the first decade of the MCNP6 code, continuous progress is being made toward a modernized, general-purpose Monte Carlo radiation transport code that remains a trusted resource for the global community of practitioners. This paper describes these first 10+ years of the MCNP6 code and its continually improving data libraries, and gives some insight into how the next decade is expected to unfold.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1994

Cloning and regulation of rat tissue inhibitor of metalloproteinases-2 in osteoblastic cells

Rat tissue inhibitor of metalloproteinases-2 (TIMP-2) was cloned from a UMR 106-01 rat osteoblastic osteosarcoma cDNA library. The 969-bp full-length clone demonstrates 98 and 86% sequence identity to human TIMP-2 at the amino acid and nucleic acid levels, respectively. Parathyroid hormone (PTH), at 10(-8) M, stimulates an approximately twofold increase in both the 4.2- and 1.0-kb transcripts over basal levels in UMR cells after 24 h of exposure. The PTH stimulation of TIMP-2 transcripts was not affected by the inhibitor of protein synthesis, cycloheximide (10(-5) M), suggesting a primary effect of the hormone. This is in contradistinction to regulation of interstitial collagenase (matrix metalloproteinase-1) by PTH in these same cells. Nuclear run-on assays demonstrate that PTH causes an increase in TIMP-2 transcription that parallels the increase in message levels. Parathyroid hormone, in its stimulation of TIMP-2 mRNA, appears to act through a signal transduction pathway involving protein kinase A (PKA) since the increase in TIMP-2 mRNA is reproduced by treatment with the cAMP analogue, 8-bromo-cAMP (5 x 10(-3) M). The protein kinase C and calcium pathways do not appear to be involved due to the lack of effect of phorbol 12-myristate 13-acetate (2.6 x 10(-6) M) and the calcium ionophore, ionomycin (10(-7) M), on TIMP-2 transcript abundance. In this respect, regulation of TIMP-2 and collagenase in osteoblastic cells by PTH are similar. However, we conclude that since stimulation of TIMP-2 transcription is a primary event, the PKA pathway must be responsible for a direct increase in transcription of this gene.

Non-NASA Center↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗