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Conference paper titled, Comparing microclimate weather conditions from the weather research and forecasting (WRF) model with typical meteorological year (TMY) data for building energy simulations accepted for Building Simulation 2025 Conference in Brisbane, Australia in Aug 2025.
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Cargo containers passing through ports are scanned by non-intrusive inspection systems to search for concealed illicit materials. By using two photon beams with different energy spectra, dual energy inspection systems are sensitive to both the area density and the atomic number of cargo contents. Most literature on the subject assumes a simple exponential attenuation model for photon intensity in which only free streaming photons are detected. However, this approximation neglects second order effects such as scattering, leading to a biased model and thus incorrect material predictions. This work studies the accuracy of the free streaming model by comparing it to simulation outputs, finding that the model shows poor atomic number reconstruction accuracy at high- Z and suffers significantly if the source energy spectra and detector response function are not known exactly. To address these challenges, this work introduces a semiempirical transparency model which modifies the free streaming model by rescaling different components of the mass attenuation coefficient, allowing the model to capture secondary effects ignored by the free streaming model. The semiempirical model displays improvement agreement with simulated results at high- Z and shows excellent extrapolation to materials and thicknesses which were not included during the calibration step. Furthermore, this work demonstrates that the semiempirical model yields accurate atomic number predictions even when the source spectra and detector response are not known exactly. In conclusion, using the semiempirical model, manufacturers can perform a simple calibration to enable more precise Z reconstruction capabilities, which has the potential to significantly improve the performance of existing dual energy radiographic systems.
Abstract Technical and non‐technical losses in distribution circuits result in significant economic costs to power utilities. One type of non‐technical loss is energy theft by various means including illegal tapping of feeders, bypassing the meter, and billing fraud. These losses are usually hard to detect, and can remain undetected for long periods of time. Machine learning models have been proven effective in detecting these conditions, but rely on the availability of large, good‐quality training data sets. The problem is exacerbated by the imbalanced nature of data related to these conditions—energy theft, though costly, is very rare. The available data sets generally have very few samples of theft with most of the data pertaining to normal operation. Such data sets are generally not suitable to train machine learning models. In this paper, an overview of energy theft detection techniques, the challenges with their data needs, and the limitations of current techniques to bridge such data limitations is presented. A co‐simulation framework is proposed to generate reliable training data for machine learning algorithms for theft detection. An example scenario is presented and a machine learning model is built to detect certain kinds of energy theft.
The discrepancy between the observed and computed flux of solar neutrinos can be eliminated if the temperature gradient in the core of the model, and hence its central temperature, is reduced. This could in principle be accomplished by reducing the core opacity; alternatively, it has been proposed that a fraction of the energy transport in the solar core results from the motion of hypothetical particles (the so-called WIMPs). The resulting changes in solar structure have measurable effects on the frequencies of the solar 5-minute oscillations. Here, models are considered where the opacity in the solar core has been decreased in a manner which roughly simulates the effects of the WIMPs. The analysis of the models and their frequencies provides insight into the consequences of modifications to the physics of solar models. In particular, it is found that models with the observed neutrino flux are inconsistent with observations of low-degree solar oscillations.
Numerical models have been widely used for the resource characterization and assessment of tidal instream energy. The accurate assessment of tidal stream energy resources at a feasibility or project-design scale requires detailed hydrodynamic model simulations or high-quality field measurements. This study applied a three-dimensional finite-volume community ocean model (FVCOM) to simulate the tidal hydrodynamics in the Passamaquoddy–Cobscook Bay archipelago, with a focus on the Western Passage, to assist tidal energy resource assessment. IEC Technical specifications were considered in the model configurations and simulations. The model was calibrated and validated with field measurements. Energy fluxes and power densities along selected cross sections were calculated to evaluate the feasibility of the tidal energy development at several hotspots that feature strong currents. When taking both the high current speed and water depth into account, the model results showed that the Western Passage has great potential for the deployment of tidal energy farms. The maximum extractable power in the Western Passage was estimated using the Garrett and Cummins method. Different criteria and methods recommended by the IEC for resource characterization were evaluated and discussed using a sensitivity analysis of energy extraction for a hypothetical tidal turbine farm in the Western Passage.
Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.
Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.
The purpose of this work is to contribute to the Integrated Energy Systems (IES) modeling efforts from the Department of Energy with Idaho National Laboratory. The DOE is working on a plug-and-play set of models in an open-source repository called HYBRID that can be used to improve IES modeling capabilities.
This study uses experiments from the Energy Exascale Earth System Model (E3SM) to compare the influence on tropical cyclone (TC) activity of: (i) the atmospheric effect of aerosols under specified sea-surface temperatures (SSTs); and (ii) the net effect of greenhouse gases (GhGs) (including changes in SSTs). The experiments were performed using the CMIP6 Shared Socioeconomic Pathway SSP5-8.5 emissions scenario with GhG-induced SST warming specified and atmospheric aerosol effects simulated but without explicit ocean coupling. Insignificant changes in global TC frequency are found in response to the atmospheric effect of future aerosols and GhGs, as significant regional responses in TC frequency counteract each other. Future GhGs contribute to more frequent TCs in the North Atlantic, and reductions over the Northwestern Pacific and Southern Indian Ocean. The atmospheric effect of future aerosols drives more frequent TCs over the Northwestern Pacific and reductions over the Northeast Pacific and North Atlantic. Along with increases in TC intensity, global TC precipitation (TCP) is projected to increase by 52.8% (14.1%/K) due to the combined effect of future aerosols and GhGs. Although both forcings contribute to TCP increases (14.7–19.3% from reduced aerosols alone and 28.1–33.3% from increased GhGs alone), they lead to different responses in the spatial structure of TCP. TCP increases preferentially in the inner-core due to increased GhGs, whereas TCP decreases in the inner-core and increases in the outer-bands in response to the atmospheric effects of decreased aerosols. These changes are distinct from those caused by aerosol-induced SST changes, which have been considered in other studies.
We study the broadband spectral energy distribution (SED) of the prototypical low-mass active galactic nucleus (AGN) in NGC 4395. We jointly model the optical through mid-IR SED with a combination of galaxy and AGN light, and find that on arcsecond scales the AGN dominates at most wavelengths. However, there is still some ambiguity about emission from the galaxy, owing partially to the strong short-term variability of the black hole. We investigate the use of smooth- and clumpy-torus models in order to disentangle the nuclear IR emission, as well as exploring the use of poloidal wind emission to account for the blue spectral slope observed in the near-IR. Even when simultaneously fitting the full optical–IR spectral range, we find that degeneracies still remain in the best-fit models. We conclude that high-spatial-resolution and wider-wavelength coverage with the JWST is needed to understand the mid-IR emission in this complex, highly variable object, which is the best nearby example to provide a blueprint to finding other low-mass AGNs via their mid-IR emission in the future.
Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.
This work will detail a proposed uncertainty quantification method for PV annual energy estimates. Motivations behind the proposed methodology will be discussed, including the frequent underperformance of installed PV systems to calculated probability of exceedance values being seen in industry. A methodology in which uncertainty factors correlated to different parts of the PV annual energy model chain are applied to annual energy estimates in conjunction with uncertainty from inter-annual solar resource variability will be described. The application of this methodology in the System Advisor Model (SAM) will also be discussed with example cases and graphical outputs of the probability of exceedance data calculated for the PV annual energy estimates.
We have been assessing the effects of background radiation in low-Earth orbit for the next generation of X-ray and Cosmic-ray experiments, in particular for International Space Station orbit. Outside the areas of high fluxes of trapped radiation, we have been using parameterizations developed by the Fermi team to quantify the high-energy induced background. For the low-energy background, we have been using the AE8 and AP8 SPENVIS models to determine the orbit fractions where the fluxes of trapped particles are too high to allow for useful operation of the experiment. One area we are investigating is how the fluxes of SPENVIS predictions at higher energies match the fluxes at the low-energy end of our parameterizations. I will summarize our methodology for background determination from the various sources of cosmogenic and terrestrial radiation and how these compare to SPENVIS predictions in overlapping energy ranges.
A particle and energy balance model of the plasma discharge in magnetic ring-cusp ion thrusters has been developed. The model follows the original work of Brophy in the development of global 0-D discharge models that utilize conservation of particles into and out of the thruster and conservation of energy into the discharge and out of the plasma in the form of charged particles to the walls and beam and plasma radiation. The present model is significantly expanded over Brophy's original work by including self-consistent calculations of the internal neutral pressure, electron temperature, primary electron density, electrostatic ion confinement (due to the ring-cusp fields), plasma potential, discharge stability, and time dependent behavior during recycling. The model only requires information on the thruster geometry, ion optics performance and electrical inputs such as discharge voltage and currents, etc. to produce accurate performance curves of discharge loss versus mass utilization efficiency. The model has been benchmarked against the NEXIS Laboratory Model (LM) and Development Model (DM) thrusters, and successfully predicts the thruster discharge loss as a function of mass utilization efficiency for a variety of thrusters. The discharge performance model will be presented and results showing ion thruster performance and stability given.
The purpose of this work is to fit a previously developed empirical equation for puncture energy to simulation data. The conservative puncture energy equation could be used to expedite the process of performing calculations in the development of safety measures, avoiding the need to create complex finite element models for specific puncture scenarios. A total of 108 simulations are developed by varying coupon thickness, coupon material, probe shape, and probe diameter. The simulations are comprised of a low-velocity probe puncturing the coupons, from which the probe kinetic energy change is calculated. The empirical equation is fit to the dimensions, material properties, and energy results using a non-linear least-squares regression method within Python, which determines the two constant parameters for each fit. More statistically significant fit results are achieved by separating the data by probe shape and coupon material.
Metal additive manufacturing technologies keep receiving a great deal of interest as well as strong requests to develop methods to link the process science to printed parts performance and understand how to overcome inherent limitations. Here, a high-fidelity model based on the multiphysics ALE3D code was developed to reproduce the directed energy deposition process down to the powder scale. This includes resolving the laser-powder-melt pool interactions (powder impingement and incorporation into melt pool, hydrodynamics flow condition and laser absorption inefficiencies) as well as the resulting solidification microstructure. This micrometer scale digital twin captured the effect of powder incorporation process and powder flow rate on porosity. Furthermore, it was used to explore how a ring laser beam profile instead of the standard Gaussian laser profile could decrease the thermal gradient along the solidification front in the melt pool, which in turn can increase propensity for more desirable equiaxed grains.
The primary goal of the work is to improve accuracy of hydrodynamic loads acting on the lifting surface of the kite turbine due to complex air-water-lifting body interaction in a low-order numerical model using high-fidelity numerical input. The result of this study will help the Participant better design control algorithms for the kite turbine so that the turbine can harvest more energy while maintaining stability of the platform as well as its reliability. The use of hydrodynamic coefficients/load components from computational fluid dynamic simulation will inform better inputs for a medium-fidelity dynamic model (specifically an OrcaFlex model) which is intended for use in rapid design iterations in the design process.