Crew State and Risk Model (CSRM) - An Integrated Physiologic Map for Exploration Class EVA Predictive Modeling and Operations
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Dwyer Roche↗
SEARCH · Engineering Papers
Results for “Model prediction”
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.
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Examining transport and integrated modeling predictive capabilities for negative-triangularity scenarios
This paper investigates the predictive capabilities of TGYRO and TGLF models in assessing the performance of negative triangularity (NT) plasmas compared to positive triangularity (PT) plasmas in fusion devices. TGYRO predicts kinetic profiles, while TGLF analyzes turbulent transport. The study reveals that TGYRO reasonably predicts NT profiles similar to PT, although it overpredicts the high-power scenarios where there is increased experimental MHD activity. TGLF analysis finds reduced linear growth rates in NT and altered flux spectra relative to PT. Additionally, the TGLF SAT0 saturation model is observed to predict high-k transport and a reduction of particle transport with the electron temperature gradient. These findings are further corroborated by core-pedestal modeling using the Stability Transport Equilibrium Pedestal workflow, showing stronger confinement improvements in NT, particularly at higher power densities for the SAT0 saturation model. Furthermore, the study underscores the importance of accurately capturing turbulence saturation mechanisms for NT in order to project its performance accurately in fusion reactors.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Path Planning: Differential Dynamic Programming and Model Predictive Path Integral Control on VTOL Aircraft Presentation Slides
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Differential Dynamic Programming↗
Application of cyclic damage accumulation life prediction model to high temperature components
A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, but it can be applied to other materials as well. The method is designed to account for the effects on creep-fatigue life of complex loading such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features of this model were developed to make it practical for application to actual component analysis, such as the ability to handle nonisothermal loading (including TMF), arbitrary cycle paths, and multiple damage modes. The CDA life prediction model was derived from extensive specimen tests conducted on cast nickel-base superalloy B1900 + Hf. These included both monotonic tests (tensile and creep) and strain-controlled fatigue experiments (uniaxial, biaxial, TMF, mixed creep-fatigue, and controlled mean stress). Additional specimen tests were conducted on wrought INCO 718 to verify the applicability of the final CDA model to other high-temperature alloys. The model will be available to potential users in the near future in the form of a FORTRAN-77 computer program.
Nelson, Richard S.↗
A manual optimal guidance scheme using a predictive model.
Manual optimal guidance scheme using predictive display applied to launch vehicles during boost for continuous generation of predicted fuel- optimal trajectory
MANUAL CONTROL↗
A manual optimal guidance scheme using a predictive model.
Manual optimal guidance scheme using predictive display applied to launch vehicles during boost for continuous generation of predicted fuel- optimal trajectory
Gilchrist, J. D.↗
Thermal barrier coating life prediction model development
This program focuses on predicting the lives of two types of strain-tolerant and oxidation-resistant thermal barrier coating (TBC) systems that are produced by commercial coating suppliers to the gas turbine industry. The plasma-sprayed TBC system is composed of a low pressure, plasma sprayed applied, oxidation resistant NiCrAlY bond coating. The other system is an air plasma sprayed yttria (8 percent) partially stabilized zirconia insulative layer.
Strangman, T. E.↗
Geographic source attribution of honey by strontium isotope analyses: Latvia and India measurements compared to model predictions in a feasibility study
Strontium isotope ratios ( 87 Sr/ 86 Sr) can be used to determine the geographic origin of agricultural products. Here, in this study, we measured the 87 Sr/ 86 Sr of honey samples from Richland, WA, USA, Latvia, and India, and compared the results to published 87 Sr/ 86 Sr from surrounding areas and values predicted based upon a random forest isoscape model (Bataille et al., 2020). While the 87 Sr/ 86 Sr of the honey samples compared well with previously published data, the model did not accurately predict the 87 Sr/ 86 Sr of the honey samples, demonstrating that further refinement of the model would be beneficial. Because Sr cycling in terrestrial ecosystems is complex, the 87 Sr/ 86 Sr of honey is likely to reflect several sources (e.g., nectar, surface water, dust) and may change over time. In order to accurately predict the 87 Sr/ 86 Sr of honey, models must consider local geology, ecology, and bee behavior. Additional research examining how Sr is incorporated into honey is needed to inform models.
60 APPLIED LIFE SCIENCES↗
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↗
Turbulence Model Predictions of Strongly Curved Flow in a U-Duct
The ability of three types of turbulence models to accurately predict the effects of curvature on the flow in a U-duct is studied. An explicit algebraic stress model performs slightly better than one- or two-equation linear eddy viscosity models, although it is necessary to fully account for the variation of the production-to-dissipation-rate ratio in the algebraic stress model formulation. In their original formulations, none of these turbulence models fully captures the suppressed turbulence near the convex wall, whereas a full Reynolds stress model does. Some of the underlying assumptions used in the development of algebraic stress models are investigated and compared with the computed flowfield from the full Reynolds stress model. Through this analysis, the assumption of Reynolds stress anisotropy equilibrium used in the algebraic stress model formulation is found to be incorrect in regions of strong curvature. By the accounting for the local variation of the principal axes of the strain rate tensor, the explicit algebraic stress model correctly predicts the suppressed turbulence in the outer part of the boundary layer near the convex wall.
Rumsey, Christopher L.↗
The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations
A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.
Glasser, M. E.↗
Life prediction modeling based on cyclic damage accumulation
A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, where initiation was defined as a 0.030 inch surface length crack. A principal engineering feature of the CDA method is the minimum data base required for implementation. Model constants can be evaluated through a few simple specimen tests such as monotonic loading and rapic cycle fatigue. The method was expanded to account for the effects on creep-fatigue life of complex loadings such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. A significant data base was generated on the behavior of the cast nickel-base superalloy B1900+Hf, including hundreds of specimen tests under such loading conditions. This information is being used to refine and extend the CDA life prediction model, which is now nearing completion. The model is also being verified using additional specimen tests on wrought INCO 718, and the final version of the model is expected to be adaptable to most any high-temperature alloy. The model is currently available in the form of equations and related constants. A proposed contract addition will make the model available in the near future in the form of a computer code to potential users.
Nelson, Richard S.↗
SHIIVER Assemby Thermal Model Prediction for Transient Test Conditions
An integrated thermal model of the Structure Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) was built in Thermal Desktop/Sinda-Fluint and used to predict thermal performance of the system with the technologies being tested from the eCryo project. The technologies tested include the vapor cooling of the structure using the boil-off and multilayer insulation (MLI) on the hydrogen tank. The thermal model is used to 1) understand system transient behaviors; 2) perform trade studies of different vapor-cooling designs; 3) define the test operations matrix, test procedures, and conditions; and 4) address thermal-related questions at different phases of SHIIVER project. Details of the thermal model are herein described, and results corresponding to the test operations are presented for four different configurations at different fill levels. The thermal performance of each configuration is predicted with the thermal model regarding the heat reduction and boil-off reduction by using vapor cooling and/or MLI on the structures attached to the hydrogen tank.
thermal modeling↗
Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria
Abstract Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ 70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ 70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.
42 ENGINEERING↗
Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade
Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.
Engineering↗
Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)
This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .
97 MATHEMATICS AND COMPUTING↗
A prediction model for bipolar RAMs in a high energy ion/proton environment
A model has been developed which predicts the relative susceptibility of bipolar RAMs to heavy ion and proton upset. During the course of evaluating this model, physical and electrical variations were also evaluated indicating that the minimum internal signal level is the primary upset susceptibility indicator. Unfortunately, all of the physical and electrical variations expected during a normal product development cycle are in direct opposition to improved high-energy particle upset tolerance. Hence, a trade-off between highly susceptible, low power (medium speed) devices must be made against the less susceptible, higher power (high speed) equivalent device, taking into account the systems trade-off with respect to system power, software, error correction procedures and/or circuit redundancy.
Myers, D. K.↗
Multiobjective Constrained Symbolic Regression for Predictive Modeling of Material Creep Behavior
When creep testing is repeated on samples of the same alloy under the same parametric conditions (i.e., stress and temperature), the resulting strain/time curves can vary from each other considerably as shown in Figure 1 [1]. The time required to creep test a material to rupture can extend to the order of years. Because of this, a numerical model that can quickly analyze the incomplete results of an ongoing experiment to predict 1) the incomplete portion of the strain/time curve leading up to the rupture point and 2) the rupture point itself would be of great utility to the materials community. Such a model has the potential to save 1) the time required to finish running the experiment to rupture 2) the associated monetary cost of finishing said experiment. Furthermore, it would be advantageous if the predictive model could give a parametric function modeling strain/time curves for material scientists to investigate the impact of the temperature and stress parameters on the resulting creep behavior. This work introduces a piecewise symbolic regression algorithm to predict the remainder of the strain/time curve. Preliminary results show good model performance.
36 MATERIALS SCIENCE↗