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↗
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 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↗
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↗
Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications
The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application
Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.
Ghosh, Sambit↗
Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation
Energy sustainability is a subject of concern to many nations in the modern world. It is critical for electric power systems to diversify energy supply to include systems with different physical characteristics, such as wind energy, solar energy, electrochemical energy storage, thermal storage, bio-energy systems, geothermal, and ocean energy. Each system has its own range of control variables and targets. To be able to operate such a complex energy system, big-data analytics become critical to achieve the goal of predicting energy supplies and consumption patterns, assessing system operation conditions, and estimating system states - all providing situational awareness to power system operators. This chapter presents data analytics and machine learning-based approaches to enable predictive situational awareness of the power systems.
24 POWER TRANSMISSION AND DISTRIBUTION↗
PAUSE: Predictive Analytics Using SPARQL-Endpoints
This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.
Sukumar, Sreenivas R↗
Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.
Chemistry↗
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.
36 MATERIALS SCIENCE↗