Results for “validation method” — Page 8

DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗

NASA NTRS1977

A generalized vortex lattice method for subsonic and supersonic flow applications

If the discrete vortex lattice is considered as an approximation to the surface-distributed vorticity, then the concept of the generalized principal part of an integral yields a residual term to the vorticity-induced velocity field. The proper incorporation of this term to the velocity field generated by the discrete vortex lines renders the present vortex lattice method valid for supersonic flow. Special techniques for simulating nonzero thickness lifting surfaces and fusiform bodies with vortex lattice elements are included. Thickness effects of wing-like components are simulated by a double (biplanar) vortex lattice layer, and fusiform bodies are represented by a vortex grid arranged on a series of concentrical cylindrical surfaces. The analysis of sideslip effects by the subject method is described. Numerical considerations peculiar to the application of these techniques are also discussed. The method has been implemented in a digital computer code. A users manual is included along with a complete FORTRAN compilation, an executed case, and conversion programs for transforming input for the NASA wave drag program.

Miranda, L. R.↗

DOE OSTI2019

Validation of an inverse model of zone air heat balance

Our work introduces the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL's Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be utilized to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2001

Follow That Satellite: EO-1 Maneuvers into Closed Formation With Landsat-7

As the Landsat-7 spacecraft continued NASA's historic program of earth imaging, begun over three decades ago, NASA launched the Earth Observing-1 (EO-1) spacecraft carrying examples of the next generation of Landsat-7 instruments. The validation method for these instruments was to have EO-1 fly in a close formation behind Landsat-7 on the same World Reference System path. From that formation hundreds of near coincident images would be taken by each spacecraft and compared to evaluate improvements in the EO-1 instruments. This paper will address the mission analysis required to launch and maneuver EO-1 into the formation with Landsat-7 where instrument validation was to occur plus a summary of completing the formation acquisition. EO-1 is required to operate one minute +/- 6 seconds behind Landsat-7 during the period of co-fly imaging with a cross track separation of within + 3 kilometers. This separation time can also be stated as a one minute +/- 6 seconds time difference in the Mean Local Time (MLT) at the descending nodes. Achieving the required MLT is heavily dependent on the time of launch. The EO-1 launch window, which had to accommodate the dual payloads of EO-1 and SAC-C, was very limited ranging from 0 to 22 seconds over the 16 day Landsat-7 WRS repeat cycle during which EO-1 was launched. Each EO-1 launch opportunity that occurred on a different day of a Landsat-7 16 day repeat cycle required a separate and distinct maneuver profile. These profiles varied significantly in duration and amount of onboard propellant required to achieve them. EO-1 launched on a day judged to have "medium" resource requirements for achieving the formation with Landsat-7. To phase EO-1 one minute behind Landsat-7 in the along track direction, a series of altitude adjusts separated by specific drift intervals were executed. Additional maneuvers slightly changed the EO-1 inclination to maintain the MLT requirements. Orbit maneuvers were planned and executed within errors of less than 1.5 percent and propellant usage was near nominal consuming 3.4 kilograms out of a launch and early orbit budget of 11 kilograms. The pre-launch 3 -sigma propellant budget allowed for 1+ years of EO-1 mission life. The success of the EO-1 launch and early orbit operations provided sufficient propellant for nearly 4 years of on orbit operations. Special action taken during the EO-1 maneuver period involved some maneuver re-planning to reduce concerns about a potential close approach between EO-1 and Landsat-7. The result of this re-planning was a safer close approach and improvement in future formation acquisition planning.

DeFazio, Robert↗

NASA NTRS2001

Follow That Satellite: EO-1 Maneuvers Into Close Formation With Landsat-7

As the Landsat-7 (LS-7) spacecraft continued NASA's historic program of earth imaging begun over three decades ago, NASA launched the Earth Observing-1 (EO-1) spacecraft carrying examples of the next generation of LS instruments. The validation method for these instruments was to have EO-1 fly in a close formation behind LS-7 on the same World Reference System (WRS) path. From that formation hundreds of near-coincident images would be taken by each spacecraft and compared to evaluate improvements in the EO-1 instruments. This paper will address the mission analysis required to launch and maneuver EO-1 into the formation with LS-7 where instrument validation was to occur plus a summary of completing the formation acquisition. Each EO-1 launch opportunity that occurred on a different day of a LS-7 16-day repeat cycle required a separate and distinct maneuver profile.

DeFazio, Robert L.↗

NASA NTRSDate not supplied

A Physics-Based Spatial Wake Interactional Model of Fixed-Wing Aircraft and Rotorcraft for Urban Air Mobility

This paper presents a study of a physics-based spatial wake interactional model for urban air mobility. A modeling method for spatial wake interaction of fixed-wing aircraft and rotorcraft is presented. The method validates an existing wake vortex model with flight data from the existing literature. The model is then adopted for both fixed-wing aircraft and rotorcraft. A lift circulation model is developed for rotorcraft to account for unsteady aerodynamics and the compressibility effect. This lift circulation model provides more realistic estimate of lift circulation for rotorcraft for used in wake vortex modeling. A vehicle-building wake interaction is studied using the method of image. The effects of wake vortex encounters on aerodynamic loads, stability, and control are examined. A flight control simulation is performed to demonstrate recovery during a simulated wake vortex encounter.

Wake Vortex Encounter↗

NASA NTRS2013

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been currently provided under current day operations. Traffic flow managers can use these predictions to identify potential MIT restrictions to eliminate (e.g., those occurring during low arrival demand periods), and to determine the days in which a significant number of restrictions may be required

Operation↗

DOE OSTI2020

A coupled polygonal DEM-LBM technique based on an immersed boundary method and energy-conserving contact algorithm

Here we present a framework of coupling polygonal discrete elements and the lattice Boltzmann method using a direct forcing immersed boundary scheme. In this technique, an energy-conserving contact algorithm is utilized to handle the interactions between convex and concave polygonal particles. The surface of a polygon is represented by discrete boundary points which includes vertices of polygonal particles and/or points interpolated from vertices. The fluid-particle coupling is obtained through the interactions of the boundary points and the imaginary fluid particles using a direct-forcing immersed boundary method. Validations of the proposed technique are made by single particle and multiple arbitrarily-shaped particle sedimentation tests, and the effect of particle shape is illustrated using a drafting-kissing-tumbling benchmark.

42 ENGINEERING↗

DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Bayesian Assimilation of Multiscale Precipitation Data and Sparse Ground Gauge Observations in Mountainous Areas

Estimating the spatial distribution of precipitation is important for understanding ecohydrological processes at catchment scales. However, this estimation is difficult in mountainous areas because ground-based observation stations are often sparsely located and do not represent the spatial variability of precipitation. In this study, we develop a Bayesian assimilation method based on data collected on the Tibetan Plateau from 1980 to 2014 to estimate monthly and daily precipitation. To accomplish this, point-scale ground meteorological observations are combined with large-scale precipitation data such as satellite observations or reanalysis data. First, we remove the terrain effects from ground observations by fitting the precipitation data as functions of elevation, and then we spatially interpolate the residuals to 5-km-resolution grids to obtain monthly and daily precipitation. Additionally, we use Tropical Rainfall Measuring Mission (TRMM) satellite observations and ERA-Interim reanalysis data. Cross-validation methods are used to evaluate our method; the results show that our method not only captures the change in precipitation with terrain but also significantly reduces the associated uncertainty. The improvements are more evident in the main river source areas on the edge of the Tibetan Plateau, where elevation changes dramatically, and in high-altitude areas, where the ground gauges are sparse compared with those in low-altitude areas. Our assimilation method is applicable to other regions and is particularly useful for mountainous watersheds where ground meteorological stations are sparse and precipitation is considerably influenced by terrain.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Real-time application of knowledge-based systems

The Rapid Prototyping Facility (RPF) was developed to meet a need for a facility which allows flight systems concepts to be prototyped in a manner which allows for real-time flight test experience with a prototype system. This need was focused during the development and demonstration of the expert system flight status monitor (ESFSM). The ESFSM was a prototype system developed on a LISP machine, but lack of a method for progressive testing and problem identification led to an impractical system. The RPF concept was developed, and the ATMS designed to exercise its capabilities. The ATMS Phase 1 demonstration provided a practical vehicle for testing the RPF, as well as a useful tool. ATMS Phase 2 development continues. A dedicated F-18 is expected to be assigned for facility use in late 1988, with RAV modifications. A knowledge-based autopilot is being developed using the RPF. This is a system which provides elementary autopilot functions and is intended as a vehicle for testing expert system verification and validation methods. An expert system propulsion monitor is being prototyped. This system provides real-time assistance to an engineer monitoring a propulsion system during a flight.

Brumbaugh, Randal W.↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRS2004

Bioavailability and Pharmacodynamics of Promethazine on Long Duration Missions to the International Space Station

Space motion sickness (SMS) is often treated in space with promethazine (PMZ). Common side effects of PMZ administration (50 mg intramuscular) on the ground are drowsiness and impaired cognitive performance. Anecdotal reports indicate that these effects are absent or less pronounced in space. This suggests that the availability of PMZ to the body (bioavailability) and/or the response of the body to PMZ (pharmacodynamics) may change during space flight. Opportunities for clinical research in space are limited. The study described here is our response to a NASA Research Announcement for proposals for flight-based research needed to improve, or answer specific questions about, diagnosis and therapy during space flight, and post-flight rehabilitation. We propose here to evaluate noninvasive methods for determining the bioavailability and pharmacodynamics of PMZ. The specific objectives of the proposed research are to 1) compare pharmacokinetic and pharmacodynamic parameters of PMZ, estimated from saliva and plasma levels after administration of PMZ, 2) estimate the relative bioavailability of the three dosage forms of PMZ that are often administered to control motion sickness symptoms in space, and 3) establish the dose-response relationship of PMZ. We will estimate the bioavailability of an intramuscular injection (IM), oral tablet, and rectal suppository of PMZ in noma1 subjects during ambulatory and antiorthostatic bed rest (ABR) conditions using novel stable isotope techniques. We will compare and contrast the bioavailability of PMZ during normal and microgravity conditions to examine changes in drug absorption and bioavailability during microgravit. Results of this study will validate methods for an approved in-flight investigation with this medication awaiting an opportunity for manifestation..

Putcha, Lakshmi↗

DOE OSTI2025

A universal implementation of radiative effects in neutrino event generators

Due to the similarities between electron-nucleus (eA) and neutrino-nucleus scattering (νA), eA data can contribute key information to improve cross-section modeling in eA and hence in νA event generators. However, to compare data and generated events, either the data must be radiatively corrected or radiative effects need to be included in the event generators. We implemented a universal radiative corrections program that can be used with all reaction mechanisms and any eA event generator. Our program includes real photon radiation by the incident and scattered electrons, and virtual photon exchange and photon vacuum polarization diagrams. It uses the “extended peaking” approximation for electron radiation and neglects charged hadron radiation. This method, validated with GENIE, can also be extended to simulate νA radiative effects. This work facilitates data-event-generator comparisons used to improve νA event generators for the next-generation of neutrino experiments. Program Title: emMCRadCorr CPC Library link to program files:https://doi.org/10.17632/hmsxg82vnf.1 Developer's repository link:https://github.com/e4nu/emMCRadCorr Licensing provisions: AGPLv3 Programming language:C++ Nature of problem: Radiative effects can significantly modify the event kinematics and the resulting cross-sections. Such effects must be accounted for when comparing event generators to eA data. Existing radiative correction codes are tailored to specific processes and topologies, and are limited to a restricted phase space defined by the spectrometer acceptance. Therefore, a more general approach is required to apply radiative corrections to semi-inclusive and exclusive eA measurements. Solution method: Our program incorporates real photon radiation from both the incident and scattered electrons, as well as virtual photon exchange and photon vacuum polarization effects. It employs the “extended peaking” approximation for electron radiation while neglecting contributions from charged hadron radiation. The code is fully decoupled from event generator codes and can be used for all event generators in the market.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Construction of MoS 2 /NiS 2 heterostructure with fast interfacial reaction kinetics for ultrafast sodium storage

Constructing heterostructure is a valid method to reinforcing sodium storage performance of transition metal chalcogenides materials. Herein, a simple, safe and controllable one step hydrothermal method is proposed to synthesize MoS 2 /NiS 2 heterostructure. Due to the difference in band gaps and work functions of MoS 2 and NiS 2 , the charges are redistributed at the MoS 2 /NiS 2 heterointerfaces, thereby accelerating the migration of electrons and Na + . The heterointerfaces provide extra active sites for storing Na + , thus increasing the sodium storage capacity of the heterostructure. Furthermore, the distinct redox potentials of NiS 2 and MoS 2 promote the structural stability of MoS 2 /NiS 2 heterostructure during the electrochemical reaction processes. Consequently, the obtained MoS 2 /NiS 2 heterostructure exhibits superior rate properties (339.4 mAh g –1 at 10 A g –1 ) and ultra-stable cycling stability (480.5 mAh g –1 after 350 cycles at 1 A g –1 ). Finally, this paper presents a valid strategy for creating heterostructure anodes with excellent sodium storage properties.

25 ENERGY STORAGE↗

DOE OSTI2019

Additive manufacturing of platinum group element (PGE) reference materials with a silica matrix

The microanalytical community has an outstanding need for platinum group element (PGE) reference materials, particularly for trace element analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS). National Institute of Standards and Technology (NIST) glasses contain Rh, Pd, and Pt, but lack Ru, Os, and Ir. Synthesis of silicate PGE standards has proven difficult due the tendency of PGEs to form metallic nuggets. Additive manufacturing methods were used to produce PGE standards with a silica matrix. Monodispersed submicron PGE-doped Stöber particles were used as feedstock materials for electrophoretic deposition (EPD). Two-cm-sized samples produced by EPD were subsequently densified by thermal processing. The homogeneity of PGEs was tested using LA-ICPMS and concentrations were measured by laser ablation and solution ICPMS. The PGE concentrations ranged from 0.5 to 3 μg/g. The inhomogeneity was at the 3% RSD level for Ru, Rh, Ir, and Os throughout and 5% for Pt and Pd in the interior of the samples. Based on LA-ICPMS analyses, the interiors of the two samples have near identical concentrations in PGEs. The samples fabricated in this study represent the most complete and homogeneous PGE standards produced with a silicate matrix. The ability to produce multiple samples with the same composition provides opportunities for validating methods, monitoring long-term reproducibility, and facilitating interlaboratory comparisons.

36 MATERIALS SCIENCE↗