Engineering topics
Smith, Scott
Publications and source records attributed to Smith, Scott.
Acceptable Knowledge Summary Report for REMOTE-HANDLED DEPLETED URANIUM INGOTS FROM MATERIALS AND FUELS COMPLEX
This Acceptable Knowledge (AK) Summary Report has been prepared for the Central Characterization Program (CCP) for remote-handled (RH) transuranic (TRU) waste generated and managed by the Materials and Fuels Complex (MFC), formerly Argonne National Laboratory-West (ANL-W) and part of the Idaho National Laboratory (INL). The waste described in this report, depleted uranium ingots (waste stream ID-MFC-DU-INGOT), was historically generated in Building 765, the Fuel Conditioning Facility (FCF), formerly, Hot Fuel Examination Facility (HFEF)-South. This AK Summary Report, along with the referenced supporting documents, provides a defensible and auditable record of AK for the designated waste stream, depleted uranium ingots, produced from the FCF electrorefining process. The references and AK source documents used to prepare this report are listed in Sections 8.0 and 9.0 respectively. The source documents cited throughout this report are identified by alphanumeric designations corresponding to a unique Source Document Tracking Number (e.g., AKA01, C001, CCE01, DR001, M001, P001, and U001).
Cutting force and stability for inserted cutters using structured light metrology
This paper describes force and stability prediction for inserted end and face mills using a reverse engineering approach. Structured light scanning is used to identify the spatial coordinates of the points that define the multiple insert cutting edges. These points are used to extract the cutting edge radius and angle and are then incorporated in a time domain simulation that predicts cutting force and tool/workpiece deflection for user-selected operating parameters. Good agreement between predicted and measured cutting forces is demonstrated. The process stability is then predicted using the validated force model and measured structural dynamics.
Dynamic stiffness modification by internal features in additive manufacturing
Dynamic stiffness, or the product of the modal stiffness and damping ratio, is an important consideration for the design of additively manufactured parts that will experience dynamic loading. Here, this paper describes a demonstration component which was designed and manufactured in two configurations using a metal wire arc additive process. The first configuration was an open channel structure, while the second contained a dynamic absorber in the internal cavity. Frequency response measurements of the two components showed a significant magnitude reduction for the modified component at the original open channel structure's natural frequency and an overall increase in dynamic stiffness. Polymer damping material was then added to further increase the dynamic stiffness.
Milling stability identification using Bayesian machine learning
This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.
Cutting force and stability prediction for inserted cutters
Here, this paper presents an approach for inserted end/face mill force and stability predictions using a reverse engineering approach. Structured light scanning is implemented to identify the spatial coordinates of the points that define the multiple insert cutting edges. These points are analyzed to extract the cutting edge radius and angle values, which are then incorporated in a time domain simulation that predicts cutting force and tool/workpiece deflection for user-selected operating parameters. Good agreement between predicted and measured cutting forces is first demonstrated. The process stability is then predicted using the validated force model and measured structural dynamics. Observations about the stability dependence on insert geometry are finally presented for a selected end mill.
Stability boundary and optimal operating parameter identification in milling using Bayesian learning
Here, this paper describes a novel Bayesian learning approach for stability boundary and optimal parameter identification in milling without the knowledge of the underlying tool dynamics or material cutting force coefficients. The paper is divided into two parts. First, a Bayesian learning method for stability lobe identification using test results is described. Each axial depth and spindle speed combination is characterized by a probability of stability which is updated using Bayes’ rule when a test result (stable or unstable) is made available. A novel likelihood function is defined which incorporates knowledge of the stability behavior. Numerical results show convergence to the analytical stability lobe diagram. Second, an adaptive experimental strategy to identify optimal operating parameters that maximize material removal rate is described. Numerical evaluation shows convergence to the optimal operating point with error less than 15 % within ten tests on average. The approach is validated using experimental results. Results show that the proposed method is an efficient and robust learning method to identify the stability lobe diagram and optimal operating parameters with a limited number of tests/data points.
Using Dense Phenotyping and Cluster Analysis to Identify Subgroups Associated with Deconditioning in Bed Rest
No abstract available
Meteorological services annual data report for 2019
This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2019. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to a limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).
Atmospheric Radiation Measurement (ARM) Aerosol Observing Systems (AOS) for Surface-Based In Situ Atmospheric Aerosol and Trace Gas Measurements
Aerosols alter Earth’s radiative budget both directly and indirectly through interaction with clouds. Continuous observations are required to reduce the uncertainties in climate models associated with atmospheric processing and the interactions between aerosols and clouds. Field observations of aerosols are a central component of the Atmospheric Radiation Measurement (ARM) Facility’s global measurements. The ARM mission goal is to “provide the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.” Since 1996, ARM has met this goal by operating Aerosol Observing Systems (AOS) for in situ measurement of aerosols. Currently the five ARM AOSs are the most comprehensive field deployable aerosol systems in the United States. The AOS suite includes seven measurement classes: number concentration, size distribution, chemical composition, radiative and optical properties, hygroscopicity, trace gases, and supporting meteorological conditions. AOSs are designed as standardized measurement platforms to enable intercomparison across the ARM Facility for regional process studies within a global context. The instrumentation and measurement capabilities of the ARM AOSs, along with a history of their design and field deployments are presented here.