Nonlinear prediction modeling for accelerated testing
Empirical equation derived from cycles-to-failure, temperature cells, and prediction of normal packs are presented.
Mcdermott, P. P.↗
SEARCH · Engineering Papers
Results for “Predictive Modeling”
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.
A CCIR-based prediction model for Earth-Space propagation
At present there is no single 'best way' to predict propagation impairments to an Earth-Space path. However, there is an internationally accepted way, namely that given in the most recent version of CCIR Report 564 of Study Group 5. This paper treats a computer code conforming as far as possible to Report 564. It was prepared for an IBM PS/2 using a 386 chip and for Macintosh SE or Mach II. It is designed to be easy to write and read, easy to modify, fast, have strong graphic capability, contain adequate functions, have dialog capability and windows capability. Computer languages considered included the following: (1) Turbo BASIC, (2) Turbo PASCAL, (3) FORTRAN, (4) SMALL TALK, (5) C++, (6) MS SPREADSHEET, (7) MS Excel-Macro, (8) SIMSCRIPT II.5, and (9) WINGZ.
Zhang, Zengjun↗
A predictive model of human performance.
An attempt is made to develop a model describing the overall responses of humans to exercise and environmental stresses for prediction of exhaustion vs an individual's physical characteristics. The principal components of the model are a steady state description of circulation and a dynamic description of thermal regulation. The circulatory portion of the system accepts changes in work load and oxygen pressure, while the thermal portion is influenced by external factors of ambient temperature, humidity and air movement, affecting skin blood flow. The operation of the model is discussed and its structural details are given.
Walters, R. F.↗
Offshore wind speed estimates from a high‐resolution rapidly updating numerical weather prediction model forecast dataset
Abstract In association with the Department of Energy–funded Position of Offshore Wind Energy Resources (POWER) project, we present results from compositing a 3‐year dataset of 80‐m (above ground level) wind forecasts from the 3‐km High‐Resolution Rapid Refresh (HRRR) model over offshore regions for the contiguous United States. The HRRR numerical weather prediction system runs once an hour and features hourly data assimilation, providing a key advantage over previous model‐based offshore wind datasets. On the basis of 1‐hour forecasts from the HRRR model, we highlight the different climatological regimes of the nearshore environment, characterizing the mean 80‐m wind speed as well as the frequency of exceeding 4, 12, and 25 m s −1 for east and west coast, Gulf of Mexico, and Great Lake locations. Preliminary verification against buoy measurements demonstrates good agreement with observations. This dataset can inform the placement of targeted measurement systems in support of improving resource assessments and wind forecasts to advance offshore wind energy goals both in New England and other coastal regions of the United States.
James, Eric P.↗
Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures
Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.
Epperson, David L.↗
Lag Model Predictions for UFAST SBLI Flowfield
Presentation for Shock Boundary Layer Interaction Workshop. Shows results for Lag turbulence model on one of the international workshop test cases the UFAST 8 degree test case. Comparison with PIV velocity measurements as well as computed tunnel wall flowfields are shown, emphasizing that the interaction is a 3D phenomena, and is reasonably well predicted.
Olsen, Mike↗
Life prediction modeling based on strainrange partitioning
Strainrange partitioning (SRP) is an integrated low-cycle-fatigue life predicting system. It was created specifically for calculating cyclic crack initiation life under severe high-temperature fatigue conditions. The key feature of the SRP system is its recognition of the interacting mechanisms of cyclic inelastic deformation that govern cyclic life at high temperatures. The SRP system bridges the gap between the mechanistic level of understanding that breeds new and better materials and the phenomenological level wherein workable engineering life prediction methods are in great demand. The system was recently expanded to address engineering fatigue problems in the low-strain, long-life, nominally elastic regime. This breakthrough, along with other advances in material behavior and testing technology, has permitted the system to also encompass low-strain thermomechanical loading conditions. Other important refinements of the originally proposed method include procedures for dealing with life-reducing effects of multiaxial loading, ratcheting, mean stresses, nonrepetitive (cumulative loading) loading, and environmental and long-time exposure. Procedure were also developed for partitioning creep and plastic strain and for estimating strainrange versus life relations from tensile and creep rupture properties. Each of the important engineering features of the SRP system are discussed and examples shown of how they help toward predicting high-temperature fatigue life under practical, although complex, loading conditions.
Halford, Gary R.↗
Instantiation of HCML Demonstrating Bayesian Predictive Modeling for Attentional Control
The research team developed models of Attentional Control (AC) that are unique to existing modeling approaches in the literature. The goal was to enable the research team to (1) make predictions about AC and human performance in real-world scenarios and (2) to make predictions about individual characteristics based on human data. First, the team developed a proof-of-concept approach for representing an experimental design and human subjects data in a Bayesian model, then demonstrated an ability to draw inferences about conditions of interest relevant to real-world scenarios. Ultimately, this effort was successful, and we were able to make reasonable (meaning supported by behavioral data) inferences about conditions of interest to develop a risk model for AC (where risk is defined as a mismatch between AC and attentional demand). The team additionally defined a path forward for a human-constrained machine learning (HCML) approach to make predictions about an individual's state based on performance data. The effort represents a successful first step in both modeling efforts and serves as a basis for future work activities. Numerous opportunities for future work have been defined.
97 MATHEMATICS AND COMPUTING↗
A prediction model of failure threshold for shear deformation in a Zr-based bulk metallic glass
The failure of bulk metallic glasses (BMGs) during plastic deformation at room temperature is abrupt and instantaneous, while the analysis of precursor information based on avalanche events helps predict catastrophic failure. An acoustic emission (AE) signal can provide accurate precursor information for material failure, due to its sensitive and high fast calculation ability. In the current study, AE monitoring tests are carried out during uniaxial compression tests of BMGs at different strain rates. The AE experimental failure threshold, E max , is proposed on the basis of AE cumulative energy, which reflects the intensity of damage evolution at different loading conditions. Compared with the critical shear band velocity (CSBV) associated with stick-slip dynamics of serrated flow, E max is a more sensitive failure parameter since it is connected with the local microscopic changes that occur during the material response process. Here, the E max is obtained prior to reaching the CSBV since the calculation of these two avalanches analysis focuses on the different stages of shear band growth. In particular, AE events are related to the “dry” friction process in the first stage, however, the CSBV is responsible for the “viscous” glide in the second stage. Therefore, Emax is not affected by the complex interactions between the shear bands during the stick-slip process. The maximum avalanche of serrated flow, S max , is proposed as the experimental failure threshold, which depends on the applied strain rate as S max ~ $\dot{ε}$ –λ . According to the relationship of E max and S max , the theoretical failure threshold, E max , follows a criterion E max = 2545$\dot{ε}$ –λ - 4468, where λ is equivalent to 0.15 for this work. Finally, combining the different calculations and AE measurements, this model gives new insights to predict the deformation failure behavior of Zr-based BMGs.
36 MATERIALS SCIENCE↗
Model predictive control of parametric excited pitch-surge modes in wave energy converters
A parametric excitation dynamic model is used for a three degrees-of-freedom (3-DOF) wave energy converter. Since the heave motion is uncoupled from the pitch and surge modes, the pitch-surge equations of motion can be treated as a linear time varying system, or a linear system with parametric excitation. In such case the parametric exciting frequency can be tuned to twice the natural frequency of the system for higher energy harvesting. A parametric excited 3-DOF wave energy converter can harvest more power, for both regular and irregular waves, compared to the linear 3-DOF. For example, in a Bretschneider wave, the harvested energy in the three modes is about 3.8 times the energy harvested in the heave mode alone; while the same device produces about 3.1 times the heave mode energy when using a linear 3-DOF model.
16 TIDAL AND WAVE POWER↗
The Martian Hadley circulation - Comparison of 'viscous' model predictions to observations
A comparison is conducted between the results of an analytical, steady, thermally-forced viscous model for the Martian atmosphere's zonally-averaged global circulation and observations of aeolian feature directions and atmospheric temperatures. While the observed northeasterly surface winds at high northern latitudes could be produced by possible CO2 fluxes from the northern polar cap, and southern latitudes' thermal structure during this storm is well reproduced, a major discrepancy is noted between predictions and observations of the middle northern latitudes' temperature structure.
Magalhaes, Julio A.↗
Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control
The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.
Artificial neural networks↗
Global models predict clouds at the wrong time of day: Does it matter for radiation and climate?
Accurate prediction of future climate change hinges upon the ability of Earth system models (ESMs) to simulate clouds and their radiative effects. Even if an ESM can simulate the correct clouds, a systematic error in the amount of sunlight reflected by clouds (and, thus, cloud radiative effect) can exist if the clouds are simulated at the incorrect time of day. In this work, we develop an analytical model connecting diurnal cloud biases to emergent mean state radiative biases. With the use of satellite observations, we demonstrate that there are errors in the time of day that clouds are occurring in ESMs that would cause bias in shortwave cloud radiative effect (SWCRE) that is greater than 45% of the total SWCRE bias, but such errors in the cloud diurnal cycle are masked by other compensating errors, indicating that these ESMs are getting the right answer for the wrong reasons.
Geosciences↗
Hanford Site Composite Analysis Special Analysis: P2R Model Predictive Contaminant Transport Simulations - Sensitivity Case
This environmental calculation file (ECF) documents the methodologies, assumptions, and results of predictive fate and transport simulations using the Plateau-to-River (P2R) model. This ECF supports the reevaluation of the representativeness of solid-waste radionuclide inventory and release rate from three solid waste sites included in the recently completed Hanford Site Composite Analysis (CA) (DOE-RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau (FY 2020)). Specifically, this ECF supports the reevaluation of the representativeness of the base case inventory and radionuclide waste release rates from three solid waste sites (i.e., 218-E-12B, 218-W-3A and 218-W-3AE) and two radionuclides (carbon-14 [C-14] and technetium [Tc-99]). These three waste sites and two radionuclides were identified as being the most significant contributors to groundwater contamination and dose in the CA at time periods after the compliance period. The release rates and footprints of the releases from the vadose zone are implemented in the fate and transport modeling utilizing the HSS package.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Comparison of model predictions with LDEF satellite radiation measurements
Some early results are summarized from a program under way to utilize Long Duration Exposure Facility (LDEF) satellite data for evaluating and improving current models of the space radiation environment in low Earth orbit. Reported here are predictions and comparisons with some of the LDEF dose and induced radioactivity data, which are used to check the accuracy of current models describing the magnitude and directionality of the trapped proton environment. Preliminary findings are that the environment models underestimate both dose and activation from trapped protons by a factor of about two, and the observed anisotropy is higher than predicted.
Armstrong, T. W.↗
Accelerating Informed Local Climate Action by Improving Access to DOE Predictive Modeling and Science-Based Tools
The threat of climate change is becoming increasingly urgent, and local governments across the United States are working to develop and implement climate action plans to reduce greenhouse gas emissions and mitigate the impacts of climate change. At the same time, the U.S. Department of Energy's Office of Science, Earth and Environmental Systems Sciences Division (EESSD) has been developing a range of tools and technologies to help predict the potential multi-faceted impacts from climate change given varying environmental and socioeconomic scenarios. However, local governments have been slow to engage with these scientific assets due to a lack of understanding and awareness. The DOE researchers interviewed highlighted three primary types of resources that could benefit local governments and climate practitioners: climate models, modeled datasets, and scientific journal articles. Although many of these resources are open source and available to the public, there were several challenges identified in translating these resources and their research insights directly to decision-makers.
54 ENVIRONMENTAL SCIENCES↗
Multi-Satellite Measurements and Model Predictions of Mesospheric and Upper Stratospheric Influences from the Very Large July 14-16, 2000, Solar Proton Event
A large solar flare with an associated coronal mass ejection occurred in mid-July and caused a very large solar proton event at the earth in the time period July 14-16, 2000. So far this is the largest solar storm of solar cycle 23. The solar proton fluxes were measured by instruments aboard the GOES-10 satellite and used in our proton energy deposition model to help quantify the energy input to the middle atmosphere during this large solar event. Using this computed energy deposition in the GSFC 2D atmospheric model resulted in a prediction of $>$ 20\% increases in HO$-(x)$ (H, OH, HO$-(2)$) and $>$ 100\% increases in NO$-(x)$ (N, NO, NO$-(2)$) constituents in the mesosphere and upper stratosphere at polar latitudes ($>$ 60 degrees geomagnetic). Both the HO$-(x)$ and NO$_fx)$ increases impacted ozone. Large atmospheric impacts have been measured with the NOAA 14 SBUV/2 instrument (0$_(3)$), the UARS HALOE instrument (NO, NO$-(2)$, 0$-(3)$), and the POAM III instrument (0$_{3}$, NO$-(2)$). Preliminary analysis indicates that measured (SBUV/2) and modelled 0$_{3}$ decreases from this solar event are generally in agreement in the Northern Hemisphere. Short-term ozone changes (during the event) indicate $\sim$ 15% reduction at 2 hPa ($\sim$ 45 km) up to $\sim$ 40% reduction at 0.5 hPa ($\sim$ 55 km). A longer-term ozone depletion of $\sim$ 5% is indicated between 4 and 2 hPa ($\sim$ 40-45 km). The middle atmospheric changes caused by this solar event were very large and occurred fairly quickly ($\sim$ 1-2 days). Such a significant natural perturbation provides a good test of our understanding of the middle atmosphere. The measured and modelled impacts of this solar event will be compared and discussed in this paper.
Jackman, Charles H.↗
Application of thermal life prediction model to high-temperature aerospace alloys B1900+Hf and Haynes 188
The results of the application of a newly proposed thermomechanical fatigue (TMF) life prediction method to a series of laboratory TMF results on two high-temperature aerospace engine alloys are presented. The method, referred to as TMF/TS-SRP, is based on three relatively recent developments: the total strain version of the method of Strainrange Partitioning (TS-SRP), the bithermal testing technique for characterizing TMF behavior, and advanced viscoplastic constitutive models. The high-temperature data reported in a companion publication are used to evaluate the constants in the model and to provide the TMF verification data to check its accuracy. Predicted lives are in agreement with the experimental lives to within a factor of approximately 2.
Halford, Gary R.↗