Search

As a novel manufacturing methodology, 3D printing or additive manufacturing (AM) attracts much more attentions for complex structure fabrication, especially for manufacturing metal parts. A number of metal AM processes have been studied and commercialized. However, most of them are costly and less accessible. This paper introduces a material extrusion based 3D printing process for making austenitic stainless steel 316L part using a metal-polymer composite filament (Ultrafuse 316LX). The stainless steel 316L metal specimens are printed by a commonly used 3D printer loaded with Ultrafuse filament, followed by an industry standard debinding and sintering process. Tests are performed to understand the material properties, such as hardness, tensile strength, and microstructural characteristics, of the stainless steel 316L material. In addition, an artifact model is designed to estimate the part shrinkage after the debinding and sintering process. It is found that the stainless steel 316L part exhibits apparent shrinkage after sintering. But using the Ultrafuse filament for 3D printing could be an alternative way of making metal AM parts.

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth System Models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The PCMDI Metrics Package (PMP) is an open-source Python software package that provides 'quick-look' objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including extremes. The PMP results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. An important priority of the PMP is to document evaluation statistics for all Historical and AMIP simulations submitted to recent phases of CMIP, providing version-controlled information for all data sets and software packages being used. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. In this paper, we present an overview of the PMP including its history to date, capabilities, recent updates, and future direction. [ABSTRACT FROM AUTHOR]

A framework for quantifying precipitation distributions at regional scales is presented and applied to CMIP 5 and 6 models. We employ the IPCC AR6 climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy, moderate, and light precipitating areas by K-means clustering of IMERG precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions, the similarity between observed and modeled frequency distributions, an unevenness measure based on cumulative amount, average total intensity on all days with precipitation, and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, and 6 observation-based products of daily precipitation. Our results indicate that many CMIP 5 and 6 models substantially overestimate the observed light precipitation amount and frequency as well as the number of precipitating days, especially over mid-latitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP 5 to 6 is shown in some regions, especially in mid-latitude regions, but it is not evident globally, and over the tropics most metrics point toward over degradation. [ABSTRACT FROM AUTHOR]

A framework for quantifying precipitation distributions at regional scales is presented and applied to CMIP 5 and 6 models. We employ the IPCC AR6 climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy, moderate, and light precipitating areas by K-means clustering of IMERG precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions, the similarity between observed and modeled frequency distributions, an unevenness measure based on cumulative amount, average total intensity on all days with precipitation, and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, and 6 observation-based products of daily precipitation. Our results indicate that many CMIP 5 and 6 models substantially overestimate the observed light precipitation amount and frequency as well as the number of precipitating days, especially over mid-latitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP 5 to 6 is shown in some regions, especially in mid-latitude regions, but it is not evident globally, and over the tropics most metrics point toward over degradation. [ABSTRACT FROM AUTHOR]