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At least 433 records · Page 24

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗

NIST Infiltration Tools for Commercial Buildings

The Department of Energy estimates that infiltration through building envelopes accounted for 6 % of the total energy consumed by commercial buildings and 21 % of the heating and cooling energy. Air barrier requirements are now being included in ASHRAE Standards 90.1 and 189.1 and other construction requirements. The energy impacts of unintended infiltration on building energy use can be significant, but current energy simulation software and design methods are generally not able to accurately account for envelope infiltration and the impacts of improved airtightness. NIST has been developing tools to better account for infiltration in building energy models. The tools that will be discussed in this paper include: CONTAM Results Export Tool; weather-correlated infiltration inputs for prototype commercial buildings and the associated Open Studio Measures; and a web-based tool to evaluate the energy savings of building envelope airtightening, which was developed with the Oak Ridge National Laboratory and the Air Barrier Association of America. This paper briefly describes each tool, their applications, and the benefits of using each. All of the tools are publicly available at no cost.

Ng, Lisa↗

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗

Binary-level Software Compatibility Tool Agreement

Application Binary Interface (ABI) compatibility is essential for system or software updates to ensure that libraries continue to function. Tools that can assess a binary or library ABI can thus be used to make predictions about compatibility, and predict downstream bugs by informing developers and users about issues. In this work, we are interested in describing a set of well-known tools for assessing ABI, and testing them in a controlled set experiments to assess tool agreement. We run 7660 smaller experiments across tools (N=30,640 total results) to evaluate not only predictions, but also each tool’s ability to provide detail about underlying issues. In this paper, along with highlighting the problem of assessing ABI compatibility and critiquing the pros and cons of currently available tools, we provide guidance to developers interested to test ABI based on our empirical results and suggestions for future work.

ABI↗

Strategic Planning and Decisions Tools to Enhance EM Mission Execution - 16552

Maximizing the success of an organization depends on many things with a key one being the focus and quality of the management decisions made by the organisation's senior leadership team. Successfully advancing an organisation's agenda requires operational, tactical, and strategic decisions. These decisions involve both decisions on specific actions, as well as decisions about planning. In addition to the day-to-day decisions of running the business, the leadership team should be allocating appropriate time toward developing organizational strategic objectives. Strategic planning is an organisation's process of defining its direction and strategies, and plotting how to accomplish those strategies. Strategic planning is essential to the long term viability of the organization. The pathway to success with strategic planning is not nearly so obvious and straight forward as with operational decisions. Organizational leaders must resist the temptation to minimize the time allocated to strategic thinking and strategic planning. Strategic thinking and strategic planning are centered around making sure that your organization is doing the right things, instead of just doing things right. Management guru Peter Drucker noted that 'There is nothing more wasteful than becoming highly efficient at doing the wrong things'. Strategic thinking is used in deciding what to do. Tactical thinking is used in deciding how to do it. Decision tools can be utilized to improve the results of the strategic planning process. In certain situations, properly selected decision tools will provide a mechanism or a process for making better decisions, or for channeling strategic thought and strategic planning toward enlightened alternative approaches. Decision tools can also provide for assigning a quantitative measure of merit or goodness, or at least a semi-quantitative measure of merit to multiple decision options. Properly selected and utilized, decision tools provide the leadership team with additional insight into the consequences of their decisions. Appropriately selected decision tools can be utilized to maximize the effectiveness of your strategic planning efforts. Decision tools can be used to quantify strategic decision options within a particular framework, providing insights into alternatives to improve an organisation's most effective execution of the DOE EM clean up mission. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Successes and Lessons Learned in the Design, Construction and Use of a Tool for Sampling the WR-1 Reactor Core - 1900z

Whiteshell Laboratories, located in Manitoba, Canada, provided research facilities for the Canadian nuclear industry since the early 1960's. The facility was centered on an organically cooled, heavy water moderated nuclear reactor in the world, Whiteshell Reactor 1 (WR-1). WR-1 was safely shut down in 1985, de-fueled and drained, and has been in a safe Storage with Surveillance (Deferred Decommissioning) state until present day. In support of an Environmental Assessment for proposed In Situ Decommissioning of WR-1, detailed characterization information regarding the reactor core components (calandria, calandria tubes, pressure tubes) was necessary to verify inventory estimates and neutron activation models produced circa 1992. The Fuel Channels of WR-1 (pressure tubes) were expected to be the most activated components of the reactor core, and to account for about 60% of the reactor core inventory. The interior surface of the fuel channel is also the most readily accessible surface within the reactor core. As such the decision was made to collect a sample of one of each of 3 Fuel Channel material types (Stainless Steel, Zr-2.5Nb, Ozhenite) for comparison to modelled estimates of neutron activation of various core materials. The expected dose rates from the samples were between 5 and 100 mSv (500-10,000 mrem) on contact based on a 200 mg sample. Sampling the Fuel Channels required verification the Fuel Channel was drained of organic coolant, removal of residual organic coolant from the Fuel Channel wall, removal of material from the interior surface of the Fuel Channel at the centerline of flux (∼25' below the operator position), and extraction of that material from the core. Several options for existing tooling and technologies, including hydraulic scrapers, and horizontal drill technologies, were looked at for their applicability to WR-1. While suitable approaches to higher radiation field sample collection, they were overly complex, costly and time consuming for the configuration at WR-1. In an effort to reduce costs, and to simplify the sampling process, a specialized and simple tool was developed to mechanically scrape a small sample of the interior wall of the Fuel Channels, collect the sample into a vial, and remove the sample from the core for analysis. The tool was designed, constructed, tested and implemented in the field entirely by Canadian Nuclear Laboratories Staff. The tool required no powered components, or electronic guidance or measurements; and was designed and constructed for a fraction of the cost of other commercially available technologies for sample collection within reactor cores. The samples were successfully collected without any injuries, contamination events or equipment failures. The tool remains operational in the event additional sampling is required. This paper provides additional details of the design, construction, testing and use of the tool and the successes and lessons learned during the various stages of design and implementation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Wire Arc Additive Manufacturing of Multi-material Parts Using H-13 Tool Steel

H-13 tools steel’s relative hardness, resistance to thermal fatigue, and high tolerance of thermal shock, make it very desirable for use in forging, pressing, casting, and extrusion processes[1]. H-13 is however typically quite expensive when compared to many other steel compositions. It is therefore desirable to encase a lower cost steel composition with H-13. This configuration allows for the benefits of H-13 tooling to be realized, at a considerably reduced cost. To demonstrate the viability of this concept, a casting tool was fabricated using a Wire Arc Additive Manufacturing (WAAM) process. Initial materials testing was conducted on a multi-material wall which consisted of one section of H-13 tool steel, and one section of 410NiNmo. These tests included scanning electron microscopy, energy dispersive X-ray spectroscopy, and hardness testing. This testing generated favorable results and subsequently a casting tool was fabricated for evaluation. Utilization of a Multi-Material WAAM process to fabricate large tooling in this manner could yield significant improvements in material cost, manufacturing agility, and supply chain complexity.

Lambert, Nathan [ORNL] (ORCID:0009000324134206)↗

Cybersecurity Framework Tool and Training Game Help Utilities ID Cyber Attack Vulnerabilities

PNNL cybersecurity experts developed two novel cyber tools to help utilities and organizations that serve them combat malicious cyber attacks. The first is the Cybersecurity Framework Tool, or CFT. This patented tool provides a way for utilities and their associated vendors to identify, protect, detect, respond to, and recover from emerging cyber threats or vulnerabilities to critical infrastructure. CFT provides an easy-to-follow set of best practices, policies, and procedures to improve the cybersecurity postures of critical infrastructure. The second tool is the Cybersecurity Training Game Simulator, a dynamic “train the trainer” tool. The game provides interactive training scenarios based on major cyberattacks that happened in the U.S. during the past decade. Both innovations are available for commercialization. Contact Commercialization Manager Peter Christensen at peter.christensen@pnnl.gov to learn more.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Experimental Investigations to Study the Effects of Microwave Treatment Strategy on Tool Performance in Turning Operation

In the present study, microwave treatment has been used to enhance the tribological properties of single-point tungsten carbide (WC) cutting tool inserts such as wear resistance and hardness. The tool hardness and cutting parameters were considered to evaluate the performance of microwave-treated WC inserts in turning operation. The optimum cutting parameters were identified using response surface method (RSM)-based desirability approach. The relationship between cutting parameters and output responses, viz. flank wear, cutting force and surface roughness, was developed using the RSM. The investigations revealed that the increase in tool hardness due to complex carbide formation results in a significant reduction in tool wear, cutting forces and improvement in the surface finish of workpiece. Further, the statistical models results were validated with the experimental results. Metallurgical properties of treated and untreated tool inserts were analyzed using scanning electron microscope, x-ray diffraction method and Vickers microhardness tests. The results highlighted the importance of microwave treatment in enhancing the machining performance in turning operation.

36 MATERIALS SCIENCE↗

Component based development of computer-aided tools for different applications

Computer-aided methods and tools are used at different stages of chemical process development. However new tools are needed to meet the current and future challenges through innovative and more sustainable solutions that the currently available tools may not be able to provide. This work focuses on component-based software architecture for development of tailor-made computer-aided tools for specific application objectives. The architecture is based on “components”, which can be model-objects that are stored in a library; collection of property models; databases; numerical solvers; design-algorithms (work-flows) etc. Furthermore, the main idea of these components is to incorporate a plug & play framework, where for a specific purpose, after the work-flow and associated data-flow are established, a specific computer-aided tool can be configured through a library of software components. Also, through the use of software components that are based on predictive model-based solution techniques, the final software can be reliably used to provide innovative and more sustainable solutions over a wide range of problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗

Advanced Simulation Tools for Muon-Based Accelerators

The project developed new software tools incorporating the most accurate theoretical calculations and experimental data available for crucial and not-yet-considered physics processes specific to muon accelerators. These new tools were applied to modeling muon ionization cooling channels for a muon collider or a neutrino factory, the facilities that have the potential to discover and explore new exciting fundamental physics but require the development of demanding technologies and innovative concepts. G4beamline, COSY Infinity, and WARP computer codes are particularly well-suited for addressing muon accelerator modeling problems. G4beamline was chosen for its flexibility and modularity, and for being one of the de facto standard codes used by the muon accelerator community. COSY was selected for speed, ability to take into account high-order nonlinear effects essential for the large beams under consideration, and availability of built-in optimization tools. WARP was chosen for its space charge calculation capabilities, speed and flexibility of implementing new lattice elements such as RF cavities. This final technical report summarizes the efforts on implementing advanced new tools in these computer codes addressing the shortcomings in modeling muon physics processes and using these tools to model muon ionization cooling channels.

43 PARTICLE ACCELERATORS↗

RABBIT: A Rapid Low Fidelity BVI Prediction Tool—Comparison and Validation using the NASA RVLT Toolchain

Rotorcraft noise source identification is at the forefront of civil rotorcraft applications with the emergence of the Urban Air Mobility (UAM) market. Blade Vortex Interaction (BVI) has been identified as one key source of noise produced by a rotor. To predict BVI occurrences for various Urban Air Mobility (UAM) configurations, the RApid Blade and Blade-Vortex InTeraction (RABBIT) tool was developed and utilized. The tool is built from a Beddoes Wake Model, and computes variables such as miss distance and BVI angle to calculate an impulse factor, which is able to visualize BVI for a given vehicle and flight condition. A complete checkout of this tool and comparison with CAMRADII and ANOPP2/AARON, is performed. A wake comparison between RABBIT and CAMRADII is presented, and BVI is compared with ANOPP2/AARON’s acoustic pressure time history to verify the tools effectiveness and accuracy. Three NASA Revolutionary Vertical Lift Technology (RVLT) concept vehicles were analyzed with increasing geometric and aerodynamic complexity, including the Quiet Single Main Rotor (QSMR), Side-by-Side, and Quadrotor. An analysis of the results concludes that RABBIT presents a low-fidelity tool that accurately predicts BVI location and intensity for multiple vehicle configurations and various flight conditions.

RABBIT↗

Navigating the Compliance Reporting Tool: EPAct State and Alternative Provider Fleet Program User Guide

State government and alternative fuel provider fleets covered under the State and Alternative Fuel Provider Fleet Program (Program) established pursuant to the Energy Policy Act of 1992 (EPAct) may use the Compliance Reporting Tool to track and report on several compliance activities. These activities include, but are not limited to, completing Standard Compliance annual reports, Alternative Compliance notices of intent, and exemption requests. Covered fleets can access the Compliance Tool through the Program's website at https://epact.energy.gov/users/sign_in. Covered fleet point of contacts should bookmark the Compliance Reporting Tool for future access. This user guide addresses general tool navigation, including: 1) Logging in to the Tool, 2) Managing Your Point of Contact and Account Information, 3) Managing Fleet Contact Information, 4) Adding New Fleets to Your Account, 5) Reporting for Entities With Multiple Fleets, and 6) Viewing Credit Trades.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

Control of machining-induced residual stress via tool geometry and process parameter modification

Distortion generated in machined, monolithic, thin-walled aerospace components due to residual stresses leads to significant material and economic waste in the manufacturing industry. Inherent residual stress (IRS) present in stock materials combines with machining-induced residual stress (MIRS) to influence the final machined part distortion. It is hypothesized that MIRS can be controlled, based on the part geometry, through deliberate cutting tool geometry and process parameter modifications to negate the effect of IRS on distortion, consequently resulting in distortion-free parts. A finite element (FE) orthogonal cutting model is developed to study how tool geometry and process parameters influence machining-induced residual stress (MIRS). Orthogonal cutting experiments are performed on Al 7075-T651 samples to measure cutting forces and MIRS. A cutting force dynamometer is used to measure forces during cutting and a novel digital image correlation (DIC) based hole drilling technique is employed to measure the near-surface residual stress (RS) in the cut samples. These data are subsequently utilized to validate the FE prediction model. Various cases of cutting simulations involving different depths of cut, tool tip radii, and rake angles are performed to study their effects on RS. Similar to prior literature, increasing the depth of cut, tool tip radius, or rake angle is found to promote the formation of near-surface tensile stresses. The competing effects of material plowing and temperature are shown to determine the type of RS at the end of the cut. Moreover, a window of variation of RS (up to ± 400 MPa) is estimated within the given range of conditions, allowing for the control of MIRS through tool and process modification.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗

Simultaneous Independent Control of Tool Axial Force and Temperature in Friction Stir Processing

Maintaining consistent tool depth relative to the part surface is a critical requirement for many Friction stir processing (FSP) applications. Force control is often used with the goal of obtaining a constant weld depth. When force control is used, if weld temperature decreases, flow stress increases and the tool is pushed up. If weld temperature increases, flow stress decreases and the tool dives. These variations in tool depth and weld temperature cause various types of weld defects. Robust temperature control for FSP maintains a commanded temperature through control of the spindle axis only. Robust temperature control and force control are completely decoupled in control logic and machine motion. This results in stable temperature, force and tool depth despite the presence of geometric and thermal disturbances. Performance of this control method is presented for various weld paths and alloy systems.

force control↗