Analytic prediction of baryonic effects from the EFT of large scale structures
We develop a method to describe analytically the effect of star formation physics on the clustering of galaxies at long distances.
79 ASTRONOMY AND ASTROPHYSICS↗
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Results for “Predictive analytics”
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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Absolute analytical prediction of photonic crystal guided mode resonance wavelengths
A class of photonic crystal resonant reflectors known as guided mode resonant filters are optical structures that are widely used in the field of refractive index sensing, particularly in biosensing. For the purposes of understanding and design, their behavior has traditionally been modeled numerically with methods such as rigorous coupled wave analysis. Here it is demonstrated how the absolute resonance wavelengths of such structures can be predicted by analytically modeling them as slab waveguides in which the propagation constant is determined by a phase matching condition. The model is experimentally verified to be capable of predicting the absolute resonance wavelengths to an accuracy of within 0.75 nm, as well as resonance wavelength shifts due to changes in cladding index within an accuracy of 0.45 nm across the visible wavelength regime in the case where material dispersion is taken into account. Furthermore, it is demonstrated that the model is valid beyond the limit of low grating modulation, for periodically discontinuous waveguide layers, high refractive index contrasts, and highly dispersive media.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A comparison of numerical and analytical predictions of the tidal stream power resource of Massachusetts, USA
The coastal waters of Massachusetts, USA encompass tidal phenomena that generate flows of sufficient magnitude for commercially viable power extraction. Here we examine the tidal power resource of the Massachusetts coastal region with two high-resolution hydrodynamic tidal models: a regional model encompassing the coastal waters of southeastern New England and a local domain model of Cape Cod Canal. Both models have been subject to comprehensive skill assessment using available surface elevation and ADCP measurements. Based on the model results, we identify five high-energy sites (Cape Cod Canal, Muskeget Channel, Quicks Hole, Robinson Hole and Woods Hole) for evaluation of the maximum extractable tidal power. The power extraction at these sites is modeled using linear momentum actuator disk theory applied to a cross-channel array of turbines. Of the sites evaluated, Muskeget Channel has the greatest resource, with an estimated maximum extractable power of 24 MW. The estimated total power available from all five sites is 44 MW. These estimates agree within 21% with predictions from analytical approaches at all sites. Potential applications for the models include: providing developers with an initial assessment of the resource, guiding observation programs for further study of the resource, and facilitating optimization of turbine array design.
16 TIDAL AND WAVE POWER↗
Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design
Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.
deep-learning↗
CMB lensing bispectrum: Assessing analytical predictions against full-sky lensing simulations
We report that cosmic microwave background (CMB) lensing is an integrated effect whose kernel is greater than half the peak value in the range 1 < z < 5. Measuring this effect offers a powerful tool to probe the large-scale structure of the Universe at high redshifts. With the increasing precision of ongoing CMB surveys, other statistics than the lensing power spectrum, in particular the lensing bispectrum, will be measured at high statistical significance. This will provide ways to improve the constraints on cosmological models and lift degeneracies. Following up on an earlier paper, we test analytical predictions of the CMB lensing bispectrum against full-sky lensing simulations, and we discuss their validity and limitation in detail. The tree-level prediction of perturbation theory agrees with the simulation only up to ℓ ~ 200 , but the one-loop order allows capturing the simulation results up to ℓ ~ 600 . We also show that analytical predictions based on fitting formulas for the matter bispectrum agree reasonably well with simulation results, although the precision of the agreement depends on the configurations and scales considered. For instance, the agreement is at the 10% level for the equilateral configuration at multipoles up to ℓ ~ 2000 , but the difference in the squeezed limit increases to more than a factor of 2 at ℓ ~ 2000 . This discrepancy appears to come from limitations in the fitting formula of the matter bispectrum. We also find that the analytical prediction for the post-Born correction to the bispectrum is in good agreement with the simulation. Finally, we conclude by discussing the bispectrum prediction in some theories of modified gravity.
79 ASTRONOMY AND ASTROPHYSICS↗
Risk-informed Predictive Analytics To Achieve Cost-effective Condition-based Monitoring And Maintenance Strategy
The research involves developing risk-informed predictive analytic capabilities to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop risk-informed predictive analytic algorithms. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation code based on Bayesian inference 2. Statistical data analysis code 3. Feature engineering code 4. Health classifier code 5. Diagnosis code 6. Prognosis code 7. Hazard code 8. Generation risk code 9. Economic code
Agarwal, Vivek↗
Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics
Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.
15 GEOTHERMAL ENERGY↗
Predictive Analytics for Behind-the-Meter Resources
This presentation discusses the development of the predictive analytics for behind-the-meter resources.
behind-the-meter resources↗
Predictive Analytics for ATD-2
This presentation describes on-going work to leverage high quality data and predictive analytics to improve understanding and performance of IADS system.
ATD-2↗
Predictive Analytics for Coordinated Optimization in Distribution Systems
This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Develop Methods and Tools using NSUF Data to Support Risk-informed Predictive Analytics
FY21 ASI summary on Develop Methods and Tools using NSUF Data to Support Risk-informed Predictive Analytics
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Behavior-Based Budget Management Using Predictive Analytics
Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently under similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.
99 GENERAL AND MISCELLANEOUS↗
An analytical prediction of pilot ratings utilizing human pilot model
In order to analytically predict pilot ratings, an evaluation method of a manual control system which consists of an aircraft and a human pilot, is proposed and examined. The method is constructed upon the assumptions that the control mission determines the critical frequency the pilot should bring to his focus, and that the degree of closed-loop stability and the human compensation necessary to attain the stability determine the human subjective evaluation of the system. As a result, a simple evaluation chart is introduced. The chart enables prediction of the subjective evaluation, if the controlled element dynamics and the mission are given. The chart is in good accord with almost all of the existing results of pilot ratings. This method has the following advantages: (1) simplicity, in a sense that the method needs to evaluate only two typical controlled element parameters, namely, the gain slope and the phase at the critical control frequency; (2) applicability to unstable controlled elements; (3) predictability of controllability limits of manual control; (4) possibility of estimating human compensatory dynamics.
Tanaka, K.↗
Analytical prediction of labyrinth-seal-flow-induced arotor excitation forces
An analytical method to calculate the rotor excitation forces arising from labyrinth seals is presented. The objective is to model the gas flow through the seal clearance passages and cavities when the rotor is positioned eccentricly relative to the stator center. The seal flow model used in the analysis yields solutions which validate the experimentally observed influence of seal parameters on seal forces reported in the literature. The analytically predicted seal pressure distributions and forces were compared with published experimental results.
Rajakumar, C.↗
A predictive analytical model of electrical transport in multi-principal-element alloys
A predictive analytical model is presented for the electrical conductivity of multi-principal-element alloys (MPEAs), including those containing aluminum, transition metals, and refractory metals. Given that the lattice parameter of the Wigner-Seitz cell of an MPEA is similarly variable to a bulk metallic glass, it is postulated that electron scattering can be approximated by a series of two-level systems. Here, the resulting reduced-order model enabled an accurate determination of electrical resistivity and electron thermal conductivity based on the scattering of electrons in a two-level system across a Bloch-potential-based virtual crystal approximation. Model results are compared to experimental four-point probe electrical resistivity measurements between 300 K and 700 K for Al 0.3 CoCrCuFeNi, CoCrFeMnNi, (CoCrFeMnNi) 0.98 W 0.02 , (CoCrFeMnNi) 0.95 W 0.05 , and Nb 4 Ta 4 V 3 Ti, for model validation.
Analytical model↗
Analytical prediction of reverse buckling pressure for thin shells
Analytical technique is developed for prediction of reverse buckling in thin curved shells. Test data indicate technique predicts actual reverse buckling pressure within 10 percent, and should be useful for analytical prediction of reversed condition failure for problems such as explosive decomposition through vacuum failure.
Martindale, D. L.↗
Comparison of analytical predictions of longitudinal short period pilot-induced oscillations with results from a simulation study of the space shuttle orbiter
An analytical analysis of conditions producing pilot induced oscillations (PIO's) was made for the space shuttle orbiter in a landing approach configuration for the task of milling the elevation angle of the line of sight to a target vehicle. The analysis yielded a value of PIO frequency and a value for the amount of total system time delay (pilot + control system) that can be tolerated before instability results. Calculations were performed showing the effect of varying the range to the target and of varying the handling qualities of the orbiter vehicle. Analytical predictions were compared with simulation results obtained using a visual motion simulator.
Riley, D. R.↗