Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model types”

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.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Leptonic scalars and collider signatures in a UV-complete model

We study the non-standard interactions of neutrinos with light leptonic scalars ( ϕ ) in a global ( B – L )-conserved ultraviolet (UV)-complete model. The model utilizes Type-II seesaw motivated neutrino interactions with an SU(2) L -triplet scalar, along with an additional singlet in the scalar sector. This UV-completion leads to an enriched spectrum and consequently new observable signatures. We examine the low-energy lepton flavor violation constraints, as well as the perturbativity and unitarity constraints on the model parameters. Then we lay out a search strategy for the unique signature of the model resulting from the leptonic scalars at the hadron colliders via the processes H ±± → W ± W ± ϕ and H ± → W ± ϕ for both small and large leptonic Yukawa coupling cases. We find that via these associated production processes at the HL-LHC, the prospects of doubly-charged scalar H ±± can reach up to 800 (500) GeV and 1.1 (0.8) TeV at the 2 σ (5 σ ) significance for small and large Yukawa couplings, respectively. A future 100 TeV hadron collider will further increase the mass reaches up to 3.8 (2.6) TeV and 4 (2.7) TeV, at the 2 σ (5 σ ) significance, respectively. We also demonstrate that the mass of ϕ can be determined at about 10% accuracy at the LHC for the large Yukawa coupling case even though it escapes as missing energy from the detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Impact of new experimental data on the C2HDM: the strong interdependence between LHC Higgs data and the electron EDM

The complex two-Higgs doublet model (C2HDM) is one of the simplest extensions of the Standard Model with a source of CP-violation in the scalar sector. It has a $\mathbb{Z}$ 2 symmetry, softly broken by a complex coefficient. There are four ways to implement this symmetry in the fermion sector, leading to models known as Type-I, Type-II, Lepton Specific and Flipped. In the latter three models, there is a priori the surprising possibility that the 125 GeV Higgs boson couples mostly as a scalar to top quarks, while it couples mostly as a pseudoscalar to bottom quarks. This “maximal” scenario was still possible with the data available in 2017. Since then, there have been more data on the 125 GeV Higgs boson, direct searches for CP-violation in angular correlations of $τ$-leptons produced in Higgs boson decays, new results on the electron electric dipole moment, new constraints from LHC searches for additional Higgs bosons and new results on $b$ → $sγ$ transitions. Highlighting the crucial importance of the physics results of LHC’s Run 2, we combine all these experiments and show that the “maximal” scenario is now excluded in all models. Still, one can have a pseudoscalar component in $hτ\overline{τ}$ couplings in the Lepton-Specific case as large as 87% of the scalar component for all mass orderings of the neutral scalar bosons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Charge conservation and higher moments of charge fluctuations

Higher moments of distributions of net charge and baryon number in heavy-ion collisions have been proposed as signals of fundamental QCD phase transitions. In order to better understand background processes for these observables, models are presented which enable one to gauge the effects of local charge conservation, decays of resonances and clusters, Bose symmetrization, and volume fluctuations. Monte Carlo methods for generating samplings of particles consistent with local charge conservation are presented and are followed by a review of simple analytic models involving a single type of charge with a constant experimental efficiency. The main model consists of thermal emission superimposed onto a simple parametrization of collective flow, known as a blast wave, with emission being consistent with individual canonical ensembles. The spatial extent of local charge conservation is parameterized by the size and extent over which charge is conserved. Here, the sensitivity of third- and fourth-order moments, skewness and kurtosis, to these parameters, and to beam energy and baryon density is explored. Comparisons with STAR data show that a significant part of the observed non-Poissonian fluctuations in net-proton fluctuations are explained by charge and baryon-number conservation, but that measurements of the STAR collaboration for fluctuations of net electric charge significantly differ from expectations of the models presented here.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Review of Tools for Modeling Core Radial Expansion in Liquid Metal-Cooled Fast Reactors

Core radial expansion in liquid-metal cooled fast reactor systems is a well-known phenomenon that produces strong reactivity feedback effects. It is crucial from a safety standpoint to design a reactor with the proper core restraint system to guide expansion of the fuel assemblies into a formation that produces negative reactivity feedback in accident conditions. However, the modeling of core radial expansion and its associated reactivity feedback is extremely challenging. Liquid metal-cooled fast reactor designers (both industry and commercial) have pointed to improved modeling of core radial expansion as a key modeling challenge for these types of reactors. The Department of Energy Nuclear Energy Advanced Modeling and Simulation (DOE-NEAMS) program has an important role to play in helping to develop a robust, predictive tool for this phenomenon. A literature review has been performed to identify current simulation capabilities applicable to the prediction of core radial expansion in liquid metal-cooled fast reactors and the resultant reactivity feedback effects. An overview as well as a detailed description of the multi-physics phenomena underlying core radial expansion is given in the first two chapters. The objective of this report is to identify existing software that has been or could be applied to the core radial expansion problem, and then to identify possible paths to develop a robust, coupled Multiphysics tool for advanced analysis, in which all three physics (radiation transport, structural mechanics, thermal fluids) are taken into account considering feedback effects from each physics to the other. Several computer codes and workflows have been developed in the United States and abroad to perform individual aspects of radial expansion calculations or to perform loosely coupled analysis. No code system currently exists which tightly and robustly couples the neutronics, thermal hydraulics, and thermal mechanical physics with enough detail to fully resolve the complex core radial expansion reactivity feedback effects. The future availability of such a code system is of vital importance to fully understanding the reactivity feedback effects that occur due to radial expansion, and consequently to optimizing the design of the core restraint system. A potential code development path forward using MOOSE-based tools is suggested in order to leverage the natural tight coupling and robustness of MOOSE-based applications. Additionally, potential gaps for modeling and common assumptions are listed which require further investigation to ensure accuracy for prediction of deformations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Utilizing HYSPLIT for Emergency Response Modeling at SRS

The threat of a disaster happening at the Savannah River Site (SRS) is low, but not zero. When an emergency arises, emergency responders utilize a variety of tools to detect, track, and mitigate the disaster. HYSPLIT is a community atmospheric dispersion model that is highly configurable and has been used operationally to simulate particle, chemical, and radionuclide releases. We have developed a preliminary user interface (UI) that configures HYSPLIT to run for a variety of Emergency Action Level (EAL) scenarios at SRS. Using a hypothetical EAL scenario of an accidental release of Pu-238 in H-area and K-area onsite, an analysis was done to show how a preliminary UI framework allows HYSPLIT to be run on a terminal without the need form an active network connection. The study was successful in multiple aspects. For the preliminary UI, users were able to input certain characteristics of the hypothetical EAL such as hours of emission (how long was Pu-238 being released), curies of release, release start time (when does the model run start), and type of radionuclide directly into the terminal. For HYSPLIT, the model was successful in being able to take the UI input, calculate the results from the inputs and output multiple figures of both total volumetric airborne concentrations and surface deposition. We used the Pu-238 Derived Intervention Level (DIL) deposition thresholds for produce, dairy, and beef for visualization of the surface deposition, showing how a release would impact ingestion pathways.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

pnnl/emp-gnn

Efficient Graph Neural Network for Predicting Molecular Properties software can compare the prediction quality with ab initio DFT results reported by the high-performance state-of-theart NWChem quantum chemistry package [1] through Mean Absolute Error (MAE) obtained by the fitting between DFT and model predictions i.e, MPNN [2]. We also demonstrated the performance benefits by grouping large molecules by atom sizes, and executing GNN models on different types of resources. Since the training times depend on the number of atoms, we demonstrate the impact of distributing the workloads on two GPUs with varying capabilities (e.g., NVIDIA A100 vs. GeForce RTX 2080 Ti) to optimize the efficiency. We are at the precipice of broad adoption of GNNs for molecular property prediction tasks; hence, our work is timely by comparing model prediction against classical approaches with the intent of providing initial screening for specific classes of molecules.

Lee, Hyungro↗
DOE OSTI2020

Implications of the Gaia sausage for dark matter nuclear interactions

The advent of the Gaia era has led to potentially revolutionary understanding of dark matter (DM) dynamics in our galaxy, which has important consequences for direct detection (DD) experiments. In this paper, we study how the empirical DM velocity distribution inferred from Gaia-sausage, a dominant substructure in the solar neighborhood, affects the interpretation of DD data. We survey different classes of operators in the nonrelativistic effective field theory that could arise from several relativistic benchmark models and emphasize that the Gaia velocity distribution could modify both the total number of events as well as the shape of the differential recoil spectra, the two primary observables in DD experiments. Employing the Euclideanized signal method, we investigate the effects of the Gaia distribution on reconstructing DM model parameters and identifying the correct DM model given a positive signal at future DD experiments. We find that for light DM with mass ~10 GeV, the Gaia distribution poses an additional challenge for characterizing DM interactions with ordinary matter, which may be addressed by combining complementary DD experiments with different targets and lowering the detection threshold. Meanwhile, for heavy DM with mass above ~30 GeV , depending on the type of DM model, there could be a (moderate) improvement in the sensitivity at future DD experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2026

Closure models for the feedback of energetic particles on plasma turbulence

Energetic particles interact with the plasma surrounding them, resonating with certain types of plasma waves to stabilize them while destabilizing others, and changing the character of the background turbulence in ways that have not been fully quantified or understood. Interaction with the turbulent background plasma is key to the acceleration of many types of energetic particles including high-energy cosmic rays, solar energetic particles, and pick-up ions. The acceleration of particles is a process that would ideally be described by a kinetic model, a type of model that follows a probability distribution function (PDF) for all particles in 7-dimensional (x, y, z, v x , v y , v z , t) space. Because of the high dimensionality of a kinetic model, simulations that solve kinetic equations use the largest computational resources currently available, and are yet unable to simulate a realistic number of particles, reach the large scales necessary for astrophysical problems, and use high-precision numerical methods. Two available alternatives to kinetic plasma models have been explored for this problem, with limited success. One is a multi-fluid model produced by a cumulant discarding closure, which evolves coupled equations for the velocity, magnetic field, and internal energy for both the background plasma and the fluid of energetic particles. However, simulations that solve multi-fluid magnetohydrodynamic (MHD) equations are able to include the interaction with energetic particles only in crude ways, typically as an add-on pressure term. The second alternative is to use a hybrid method to couple a fluid description of the background plasma to a kinetic model or a Fokker–Planck model for the energetic particles. These methods are hampered by the physical modeling of the coupling. In this work, we develop a new model, which follows the PDF for all particles; this can be viewed as a step toward physical realism above a multi-fluid MHD model, while also being more computationally efficient than a kinetic model. The equations we develop model both the background plasma and the energetic particles self-consistently. Over the last decade, similar PDF methods have been developed to a high level of sophistication to model reactive flows and turbulent combustion for engineering applications. For treatment of the feedback of the energetic particles on a background plasma, a PDF closure approach should evaluate the mean characteristics, including the density, with better statistical quality than will particle-sampling procedures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Superdiffusion resilience in Heisenberg chains with two-dimensional interactions on a quantum processor

Superdiffusive spin transport in the one-dimensional (1D) Heisenberg model is a key theoretical discovery in nonequilibrium quantum many-body physics. Although extensively studied in 1D systems, the breakdown and sustenance of superdiffusion in two-dimensional (2D) lattices with integrability-breaking terms, as found in real materials, remains an open question. To address this, we develop a toy model that extends the 1D Heisenberg model with a representative set of 2D interaction types and tunable strengths. Our model exhibits varying degrees of superdiffusion breakdown depending on the interaction type, spanning ballistic to diffusive regimes. We establish and justify a hierarchy of 2D interactions based on their resilience against superdiffusion breakdown: Heisenberg >𝑋⁢𝑋 > Ising. This precise control over the superdiffusive behavior also enables rigorous benchmarking of quantum hardware, and our simulations on IBM's Heron devices confirm the hardware's ability to accurately capture these many-body nonequilibrium phenomena. Overall, our results are relevant not only to simulating superdiffusion in real materials, such as the 1D Heisenberg compound KCuF3, which contains modest nonintegrable 2D terms, but also to extending superdiffusive behavior to larger 2D qubit lattices and other 2D materials.

Alagarsamy Manikandan, Keerthi Kumaran [ORNL]↗
DOE OSTI2023

Search for Majorana neutrinos in same-sign WW scattering events from pp collisions at $\sqrt{s}=13$ TeV

A search for Majorana neutrinos in same-sign WW scattering events is presented. The analysis uses $\sqrt{s}=13$ TeV proton–proton collision data with an integrated luminosity of 140 fb -1 recorded during 2015–2018 by the ATLAS detector at the Large Hadron Collider. The analysis targets final states including exactly two same-sign muons and at least two hadronic jets well separated in rapidity. The modelling of the main backgrounds, from Standard Model same-sign WW scattering and WZ production, is constrained with data in dedicated signal-depleted control regions. The distribution of the transverse momentum of the second-hardest muon is used to search for signals originating from a heavy Majorana neutrino with a mass between 50 GeV and 20 TeV. No significant excess is observed over the background expectation. The results are interpreted in a benchmark scenario of the Phenomenological Type-I Seesaw model. In addition, the sensitivity to the Weinberg operator is investigated. Upper limits at the 95% confidence level are placed on the squared muon-neutrino–heavy-neutrino mass-mixing matrix element |V μN | 2 as a function of the heavy Majorana neutrino’s mass m N , and on the effective μμ Majorana neutrino mass |m μμ |.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Spheres to jets tuning event shapes with 5d simplified models

Hidden sectors could give rise to a wide variety of events at the LHC. Confining hidden sectors are known to engender events with a small number of jets when they are weakly-coupled at high energies, and quasi-spherical soft unclustered energy patterns (SUEPs) when they are very strongly-coupled (large ‘t Hooft coupling) at high energies. The intermediate regime is murky, and could give rise to signals hiding from existing search strategies. While the intermediate coupling regime is not calculable, it is possible to pursue a phenomenological approach in which one creates signals that are intermediate between spherical and jetty. We propose a strategy for generating events of this type using simplified models in extra dimensions. The degree to which the event looks spherical is related to the number of decays produced near kinematic threshold. We provide an analytic understanding of how this is determined by parameters of the model. To quantify the shape of events produced with this model, we use a recently proposed observable - event isotropy - which is a better probe of the spherical regime than earlier event shape observables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multi-module-based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the $\mathcal{normal}$ waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several Artificial Neural Network (ANN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Testing the Froggatt-Nielsen mechanism with lepton flavor and number violating processes

The Froggatt-Nielsen (FN) mechanism offers an elegant explanation for the observed masses and mixings of Standard Model fermions. In this work, we systematically study FN models in the lepton sector, identifying a broad range of charge assignments (“textures”) that naturally yield viable masses and mixings for various neutrino mass generation mechanisms. Using these textures, we consider higher-dimensional operators consistent with a FN origin and find that natural realizations predict distinct patterns in lepton flavor- and number-violating observables. For Dirac and Majorana neutrinos, FN-related correlations can lead to detectable rates of charged lepton flavor violation at next-generation low-energy experiments. Majorana and type-I seesaw models predict measurable rates of neutrinoless double beta decay. Determination of inverted neutrino mass ordering would exclude the Dirac neutrino FN scenario. Only a small minority of purely leptonic FN models predict detectable flavor violation at future muon colliders, though it is possible that a combined analysis with the quark sector will reveal motivated signals. These findings highlight the power of the FN mechanism to link neutrino mass generation to testable leptonic observables, offering new pathways for the experimental exploration of lepton number and underscoring the importance of next-generation low-energy probes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

High-fidelity multiphysics load following and accidental transient modeling of microreactors using NEAMS tools: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts

The feasibility of modeling microreactors using high-fidelity models with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) tools is investigated in this report. Three overarching questions guided this research: can NEAMS tools readily be applied for high-fidelity multiphysics modeling of different types of transients in microreactor designs; how accurate are the results obtained; and are improvements needed in accuracy or user experience of NEAMS tools, especially considering newly developed capabilities? This work builds upon FY-2022 work, and two microreactor concepts considering heat pipe (HP-MR) and gas-cooled (GC-MR) technologies were further analyzed using high-fidelity multiphysics simulations. The NEAMS tools considered and coupled within the MultiApp environment are Griffin for neutronics, BISON for thermo-mechanics, Sockeye for heat pipe modeling (in HP-MR), SAM for 1D Fluid – 3D solid modeling of coolant channels and system modeling of balance of plant components (in GC-MR), and the SWIFT code for hydrogen redistribution in hydride moderator. The Heat Pipe MicroReactor (HP-MR) concept was further analyzed in FY-2023 to demonstrate the stochastic TRISO failure modeling capability in BISON to check operational limits of the TRISO fuel. A new full-core Gas-Cooled MicroReactor (GC-MR) model was developed based on the initial assembly-model used in Y-2022 and used for steady-state and accidental depressurization transient simulations. Accuracy of the simulations performed was assessed through 1) verification analyses completed on the different physics with code-to-code comparison, and 2) validation of the multiphysics simulations based on modeling of the Kilopower Reactor Using Stirling Technology (KRUSTY) experiment. In FY-2023, the mesh and model of KRUSTY was updated to closely match publicly available data, and the neutronic model was verified and validated against experimental control rod worth measurements. The multiphysics model of KRUSTY was developed and used for steady-state analysis and for modeling reactivity insertion transient. The calculated power increase and stabilization agrees well with experimental data following adjustment in fuel thermal expansion coefficient. As an important component of this project, the ANL team gathered experience with a wide range of NEAMS tools: the MOOSE Mesh System, Griffin, BISON, SWIFT, Sockeye, SAM, Workbench, and the MOOSE MultiApp System, and provided assessment of new capabilities. Noteworthy are the user assessment of the “vapor-only” flow model in Sockeye and development of a multiphysics startup transient in HP-MR unit cell for use as tutorial in Sockeye. The full-core GC-MR model was used for assessment of SAM for balance of plant modeling and for demonstrating the SWIFT code capability for hydrogen redistribution modeling in multiphysics transient analyses. In this process, several bugs/issues were identified and reported to developers. Finally, the assembly GC-MR model developed in FY-2022 coupling Griffin, BISON and SAM through flow blockage and rod ejection transients was published to the National Reactor Innovation Center (NRIC) Virtual Test Bed (VTB). The Heat Pipe MicroReactor (HP-MR) concept high-fidelity multiphysics coupling of Griffin/BISON/Sockeye in load-following and heat pipe failure transients was also published on the VTB. Those submissions are enabling thorough review of these models as well as wide distribution to industry, regulator, and university users. In this analysis, several new research questions were uncovered, and follow-up analyses are recommended to further improve some models, consider additional transients, and continue development of VTB models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗