Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model calibration tasks”

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 109 records · Page 6

Toward an IMU-based Space Suit Motion Capture System

Spacesuits are complex engineering systems that sustain human health and enable performance outside of Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA on the lunar surface during the Artemis program is expected to be more frequent and require higher physical workloads than previous EVAs during the ISS, Shuttle, or Apollo programs. Hence it is important to optimize future as well as current spacesuits to be efficient and comfortable for the success of space and planetary missions. To enable this, an efficient method is needed to test these spacesuits on the ground.When testing spacesuits in ground environments, it is often necessary to understand the kinematics of the suit to validate the design against relevant requirements or characterize the physical workload necessary to operate the suit. This is a challenging task for traditional optical motion capture (OMC) approaches: suit-mounted OMC markers are easily occluded by the subject or environment and may become detached during testing. Controlling lighting and reflectivity of objects in the motion capture volume is also difficult. Fixed-position OMC cameras also constrain testing to a small and contrived laboratory environment, disallowing kinematics capture in field environments.One promising alternative is the use of suit-mounted inertial measurement units (IMUs). These sensors are small, unobtrusive, and portable, but come at the cost of increased sensor noise and complexity of the software and mathematics to analyze the collected data. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture methodand inverse kinematics solver which relies solely on a network of wireless IMUs attached to the major kinematic segments of the spacesuit. The ASIK modeling language allows for the simple inclusion of probabilistic priors such as suit size and shape or IMU positions and rotations. Furthermore, to increase accuracy and reduce operational overhead to use this motion capture approach, the developed inverse kinematics solver exploits so-called self-calibratingalgorithmic techniques, which reduce the need for precise alignment of the sensors on the segments or scripted functional calibration procedures. The ASIK system was tested in a 7-subject pilot study. Each subject donned NASA’s new prototype exploration spacesuit in the Active Response Gravity Offload System (ARGOS) facility at the NASA Johnson Space Center. The subjects were outfitted with a set of 14 APDM (Portland, OR, USA) Opal IMUs, 12 of which were used in the ASIK model to estimate lower body and trunk kinematics. The subjects were also outfitted with a set of reflective OMC markers and traditional OMC data was collected and processed. Presented results will include characterization of ASIK-derived suit joint angles accuracy against an optical motion capture datum. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.If successful, IMU-based motion capture will enable testing and validation of spacesuits more frequently, with less overhead, in more extreme environments. Future work will apply these techniques to common spacesuit testing tasks, such as gait, mobility, and balance assessment, physical workload characterization, and ergonomics evaluations.

Timothy Mcgrath↗

ARM Aerosol Measurement Science Group 2019 Strategic Planning Workshop Report

This report summarizes the results of a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility workshop held in November 2019 to advance a science-based strategy for ARM’s aerosol measurement program. This was the second such workshop since the Aerosol Measurement Science Group (AMSG) was chartered in 2015 to enhance coordination of ARM observations of aerosols and atmospheric trace gases with the needs of ARM users. The results presented here reflect the AMSG’s focus in recent years on science-based strategies that will contribute to the increased use of ARM data to fulfill its mission of improving process representations and predictability in climate models. Sessions held during the workshop range from interfacing with models through aerosol sampling strategies to calibration protocols and data products. The strategies set forth here were also developed to be directly relevant to ARM’s updated Decadal Vision. The AMSG workshops have been designed to recommend actions that will enable ARM to evolve and continue to meet its mission. To that end, the AMSG will develop an actionable plan from the recommendations outlined here. Some are well defined and can reasonably be accomplished in the short term. Others are less definite or of a larger scope that calls for a longer-term implementation. Further discussion will be required to develop and prioritize actionable items related to such areas. Task teams comprising the appropriate expertise and perspective from the AMSG and other members of the community will be formed to achieve this outcome. Some particular topics are recognized as high priority, so plans are underway to develop task teams and to hold follow-on discussions to address them. Four areas currently being considered for short, focused discussion are 1) aerosol measurements on the North Slope of Alaska, 2) improving data usability for modeling, 3) strategies for advancing remote sensing, vertical profiling, and distributed measurements of aerosols, and 4) aerosol sampling strategies at existing ARM sites to provide intensive modes of operation to promote data usage for process and modeling studies.

54 ENVIRONMENTAL SCIENCES↗

Exploring the role of judgement and shared situation awareness when working with AI recommender systems

Abstract AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.

Srivastava, Divya↗

Heat flux instrumentation for HYFLITE thermal protection system

Tasks performed in this project were defined in a September 9, 1994 meeting of representatives of Vatell, NASA Lewis and Virginia Tech. The overall objective agreed upon in the meeting was 'to demonstrate the viability of thin film techniques for heat flux and temperature sensing in HYSTEP thermal protection systems'. We decided to attempt a combination of NASA's and Vatell's best heat flux sensor technology in a sensor which would be tested in the Vortek facility at Lewis early in 1995. The NASA concept for thermocouple measurement of surface temperature was adopted, and Vatell methods for fabrication of sensors on small diameter substrates of aluminum nitride were used to produce a sensor. This sensor was then encapsulated in a NARloy-Z housing. Various improvements to the Vatell substrate design were explored without success. The basic NASA and Vatell sensor layouts were analyzed by finite element modeling, in an attempt to better understand the effects of material properties, dimensions and thermal differential element location on sensor symmetry, bandwidth and sensitivity. This analysis showed that, as long as the thermal resistivity of the thermal differential element material is much larger (10X) than that of the substrate material, the simplest arrangement of layer is best. During calibration of the sensor produced in this project, undesirable side-effects of combining the heat flux and temperature sensor return leads were observed. The sensor did not cleanly separate the heat flux and temperature signals, as sensors with four leads have consistently done before. Task 7 and 8 discussed in the meeting will be performed with a continuation of funding in 1995. The following is a discussion of each of the tasks performed as outlined in the statement of work dated september 26, 1994. Task 1A was added to cover further investigation into the NASA sensor concept.

Diller, T. E.↗

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Art of Space Flight Exercise Hardware: Design and Implementation

The design of space flight exercise hardware depends on experience with crew health maintenance in a microgravity environment, history in development of flight-quality exercise hardware, and a foundation for certifying proper project management and design methodology. Developed over the past 40 years, the expertise in designing exercise countermeasures hardware at the Johnson Space Center stems from these three aspects of design. The medical community has steadily pursued an understanding of physiological changes in humans in a weightless environment and methods of counteracting negative effects on the cardiovascular and musculoskeletal system. The effects of weightlessness extend to the pulmonary and neurovestibular system as well with conditions ranging from motion sickness to loss of bone density. Results have shown losses in water weight and muscle mass in antigravity muscle groups. With the support of university-based research groups and partner space agencies, NASA has identified exercise to be the primary countermeasure for long-duration space flight. The history of exercise hardware began during the Apollo Era and leads directly to the present hardware on the International Space Station. Under the classifications of aerobic and resistive exercise, there is a clear line of development from the early devices to the countermeasures hardware used today. In support of all engineering projects, the engineering directorate has created a structured framework for project management. Engineers have identified standards and "best practices" to promote efficient and elegant design of space exercise hardware. The quality of space exercise hardware depends on how well hardware requirements are justified by exercise performance guidelines and crew health indicators. When considering the microgravity environment of the device, designers must consider performance of hardware separately from the combined human-in-hardware system. Astronauts are the caretakers of the hardware while it is deployed and conduct all sanitization, calibration, and maintenance for the devices. Thus, hardware designs must account for these issues with a goal of minimizing crew time on orbit required to complete these tasks. In the future, humans will venture to Mars and exercise countermeasures will play a critical role in allowing us to continue in our spirit of exploration. NASA will benefit from further experimentation on Earth, through the International Space Station, and with advanced biomechanical models to quantify how each device counteracts specific symptoms of weightlessness. With the continued support of international space agencies and the academic research community, we will usher the next frontier in human space exploration.

Beyene, Nahom M.↗

Evaluation of turbo-propulsion simulators as a testing technique for fighter aircraft

Ames Research Center has under way a program to develop the technology for using turbine-powered jet engine simulators as a test technique for simulating installed jet engine characteristics in small-scale wind-tunnel models of complete VSTOL fighter configurations. The program consists of three key elements: (1) static testing SN003 prototype turbine engine simulator (MAPS), (2) the development of the Propulsion Simulator Calibration Laboratory at Ames, and (3) the design, fabrication, and testing of a twin-engine 'closely coupled' V/STOL fighter wind-tunnel model. The model will use the Compact Multi-mission Propulsion Simulator (CMAPS) and will also be tested in flow-through and jet-effects modes to assess the effect of simultaneous inlet and nozzle flow simulation. This paper includes a description of the planned effort and anticipated future tasks.

Bailey, R. O.↗

Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the "z spec " subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (~50 deg 2 ) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

79 ASTRONOMY AND ASTROPHYSICS↗

Transportation and Systems Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. An analyst using NGSAM has the ability to define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions on and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams had been working closely for several years before formalizing this collaboration. START provides the SNF routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM, as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Transportation and System Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. An analyst using NGSAM can define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide the visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions at and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams have been working closely for several years before formalizing this collaboration. START provides the SNF transportation routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

From Well Log to Formation Model: A Novel Laboratory Calibrated Methodology with Demonstration

This work demonstrates how the characterization and modeling of both elastic and creep properties are essential to describe zones in layered rock formations as either low-stress targets for stimulation or high-stress barriers to fracture growth. Prediction of fracture height is critical for designing stimulation operations in oil and gas wells. Ideally, fractures are placed in target zones which will produce hydrocarbons and should not propagate into zones expected to be unproductive or to produce unwanted fluids such as water which in turn must be treated and/or disposed. The essential task in designing stimulation plans is predicting which zones have low horizontal stresses and which will be high-stress barriers to fracture growth. Despite this importance, there are gaps in current knowledge and a complete workflow from laboratory characterization to a finite element model which includes time dependent rock deformation is required. While the research and methodology presented here also have application to CO 2or hydrogen storage, wastewater injection, and geothermal applications, the focus will be on hydrocarbon extraction. This thesis presents the results of a characterization-to-prediction workflow for the Caney shale, which is an emerging hydrocarbon resource in Oklahoma, USA. It begins with an investigation to enable critical evaluation of the Caney zonation into nominally “brittle” and “ductile” zones based on properties observed from well logs. It shows none of the zones are consistently “brittle” or “ductile” mechanical behavior based on the variety of definitions of these terms. However, the nominally ductile zones are weaker and more prone to creep. A laboratory investigation of samples including strength, elastic, and creep properties, is then used in a finite element model of stress evolution. The model includes both elastic deformation and viscoplastic creep. Results predict the least creep-prone layers to have the lowest horizontal stresses, therefore comprising hydraulic fracturing targets. The most creep-prone layers attain a horizontal stress similar to the vertical stress and therefore are predicted to be high stress barriers to hydraulic fracture stimulation. In addition to defining stimulation target intervals, the model shows how as tectonic strain rate increases, there is a transition from creep-dominated stresses to stresses dominated by elasticity.

Benge, Margaret↗

A study of remote sensing as applied to regional and small watersheds. Volume 2: Supporting technical details

The Stanford Watershed Model, the Kentucky Watershed Model and OPSET program, and the NASA-IBM system for simulation and analysis of watersheds are described in terms of their applications to the study of remote sensing of water resources. Specific calibration processes and input and output parameters that are instrumental in the simulations are explained for the following kinds of data: (1) hourly precipitation data; (2) daily discharge data; (3) flood hydrographs; (4) temperature and evaporation data; and (5) snowmelt data arrays. The Sensitivity Analysis Task, which provides a method for evaluation of any of the separate simulation runs in the form of performance indices, is also reported. The method is defined and a summary of results is given which indicates the values obtained in the simulation runs performed for Town Creek, Alabama; Alamosa Creek, Colorado; and Pearl River, Louisiana. The results are shown in tabular and plot graph form. For Vol. 1, see N74-27813.

Source record↗

Light Water Reactor Sustainability Program: Use of Time Distributions to Predict Operator Procedure Performance in Dynamic Human Reliability Analysis

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework affords software capable of conducting human reliability analysis (HRA) using a dynamic approach built around operating procedures (OPs) from nuclear power plants (NPPs). Previous HUNTER reports document the development of this software tool, the coupling of HUNTER to the simulator code, the collection of operator performance data by using simulators to calibrate HUNTER models, and linking HUNTER to probabilistic risk assessment (PRA) software. The present report largely addresses two topics. The first is a new function in HUNTER called the HUNTER Procedure Performance Predictor (P3). HUNTER P3 uses HUNTER’s built in Monte Carlo tools featuring human performance variability to identify potential error traps in procedures. The second topic is time distribution analysis to generate time inputs for dynamic HRA. The current analysis was performed to investigate time distributions for task primitives, which are the minimum task unit of analysis used in dynamic HRA modeling. Using the time distribution data, the elapsed time for human actions in an extended loss of AC power (ELAP) scenario is then investigated. Time data and prediction are essential for modeling procedure performance.

99 GENERAL AND MISCELLANEOUS↗

An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN)

This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

17 WIND ENERGY↗

Photogrammetry Applied to Wind Tunnel Testing

In image-based measurements, quantitative image data must be mapped to three-dimensional object space. Analytical photogrammetric methods, which may be used to accomplish this task, are discussed from the viewpoint of experimental fluid dynamicists. The Direct Linear Transformation (DLT) for camera calibration, used in pressure sensitive paint, is summarized. An optimization method for camera calibration is developed that can be used to determine the camera calibration parameters, including those describing lens distortion, from a single image. Combined with the DLT method, this method allows a rapid and comprehensive in-situ camera calibration and therefore is particularly useful for quantitative flow visualization and other measurements such as model attitude and deformation in production wind tunnels. The paper also includes a brief description of typical photogrammetric applications to temperature- and pressure-sensitive paint measurements and model deformation measurements in wind tunnels.

Liu, Tian-Shu↗

MTRE

Multi-Token Reliability Estimation (MTRE) is a lightweight, white-box hallucination detector for vision-language models. Instead of using only the first output token, MTRE aggregates logits from the first ~10 tokens and feeds them to a small attention-based reliability head; per-token scores are combined via a sequential log-likelihood-ratio test with early-stopping, and an MTRE-t variant calibrates thresholds via cross-fitting. MTRE reports average gains of +9.4% Accuracy and +14.8% AUROC over common baselines across MAD-Bench, MM-SafetyBench, MathVista, and arithmetic/counting tasks, while adding ~4.3M params and ~1% inference overhead (~26 MB VRAM, ~0.94 ms per detection). Key limitation: requires access to early token logits and is evaluated on a handful of open-source 7B VLMs.

Bhattarai, Manish [Los Alamos National Labs]↗

A digital algorithm for characteristic film curves

The task of establishing a film calibration scheme for magnitude studies of Skylab photographic images of Comet Kohoutek is examined. Since the data are recorded in terms of film density and have to be used in terms of exposure, the conversion from density to exposure is critical. In this film calibration scheme, the hardware deals with the data sources, recording medium, and data conversion to a computer compatible program, whereas the software deals with signal to noise enhancement, stepwedge calibration curve and leads to modeling of the film characteristic curves. A mathematical model of the characteristic curve is obtained using a modified version of Efroymson's (1960) stepwise multiple linear regression algorithm, which gives log exposure as a function of density. The difference in the calibration curves from pre- and postflight exposures is well accounted for in the model as a result of sensitive statistical tests. The characteristic curve modeling program requires about 4K of core and is executed in about 3 min.

Buckner, J.↗