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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. In conclusion, the experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

Autotuning↗
DOE OSTI2020

Issue Summary of INL Phase IV Transient Results for IAEA CRP on HTGR UAM Benchmark

This report details the Parallel and Highly Innovative Simulation for Idaho National Laboratory (INL) Code System (PHISICS)/Reactor Excursions and Leak Analysis Program (RELAP5)-3D results obtained for the transient core exercises defined for Phase IV of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) on high-temperature gas cooled reactor (HTGR) uncertainty analysis in modeling (UAM). The Phase III models and results are linked to the earlier Standardized Computer Analyses for Licensing Evaluation (SCALE)/Sampler/New ESC-based Weighting Transport (NEWT) data generated for the lattice physics (lattice) stage Phase I of the CRP. The focus of this report is the Uncertainty/Sensitivity Assessment (U/SA) of the prismatic modular high-temperature gas cooled reactor (MHTGR)-350 design, and specifically for Exercises IV-1 and IV-2 of the benchmark: the Control Rod Withdrawal (CRW) and Pressurised Loss of Cooling (PLOFC) events. The statistical U/SA methodology is implemented and demonstrated using the RAVEN code, based on perturbed cross-section libraries obtained from the SCALE/Sampler sequence. Uncertainties in nuclear data (cross-sections and the average number of neutrons produced per fission, 235U[¯v ]) lead to standard deviations (uncertainties of one s) of approximately 0.5% in the core eigenvalues of the MHTGR-350 and core models. For the coupled neutronics/thermal fluid model, local power density uncertainties up to 3.6% were observed in the colder regions of the core, while the local maximum fuel temperature uncertainties reached 1.5% for the models that included thermal fluid uncertainties. The addition of thermal fluid uncertainties dominated the impacts of nuclear data uncertainties in all cases. The main contributors to uncertainties in the power density and fuel temperatures during the transients were uncertainties in the reactor operating conditions (total power, inlet mass flow rate and inlet gas temperature). Variations in the bypass flows did not have significant impact on any of the output variables. For the nuclear data uncertainties it was found that the 235U(¯v ) / 235U(¯v ) covariance produced the largest sensitivities in terms of its impact on the eigenvalue and peak reactor power. It was also observed that the impact of any nuclear data uncertainties on the maximum fuel temperature was much less significant that the impact on eigenvalue and power. Another important finding was that although the use of eight or more energy groups is recommended for best-estimate HTGR simulation, two-group models produced acceptable uncertainty and sensitivity results for most FOMs. Since the statistical U/SA methodology is computationally expensive, and most transient solver requirements will scale directly with the number of energy groups, two energy groups could be used by HTGR developers during the early stages of design when larger uncertainty margins can be tolerated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-Time Dyson Expansion

Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on subfemtosecond time scales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body nonequilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost─through approximations and numerical techniques─while preserving the key dynamical correlations. The ultimate goal is to enable first-principles simulations of time-dependent systems ranging from small molecules to large, periodic, multidimensional solids. Here, in this perspective, we outline key challenges in developing practical simulations for time-resolved spectroscopy, with a particular focus on Green's function methodologies. We highlight a recent advancement toward a scalable framework: the real-time Dyson expansion (RT-DE) [Phys. Rev. Lett. 2024, 133, 226902]. We introduce the theoretical foundation of RT-DE and discuss strategies for improving scalability, which have already enabled simulations of system sizes beyond the reach of previous fully dynamical approaches. We conclude with an outlook on future directions for extending RT-DE to first-principles studies of dynamically correlated, nonequilibrium systems.

Reeves, Cian C. [Univ. of California, Santa Barbar↗
DOE OSTI2023

High-Throughput Electric-Field-Assisted Sintering and Characterization Techniques for Materials Discovery

Despite improvements in computing and modeling capabilities, the performance of new materials, particularly those which deviate greatly in composition from well-studied materials (e.g., high-entropy alloys), can be difficult to simulate given the lack of available experimental property data. While some modeling techniques may attempt to predict the properties of these exotic materials, most are forced to make extrapolations from more traditional materials. To fulfill the need for accelerated material synthesis and property measurement, a high-throughput methodology has been developed. Utilizing electric-field-assisted sintering (EFAS), also known as spark plasma sintering (SPS), equipped with custom tooling, samples of differing alloy compositions can be produced simultaneously as a single alloy array. Several arrays have been produced with compositions spanning the Co-Cr-Fe-Mn-Ni alloy family, including many high-entropy alloys, while the novel array geometry has enabled the samples to be polished and characterized in parallel, using X-ray diffraction, scanning-electron microscopy, and laser-based thermal diffusivity measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Project Title: Adaptive Vertical Grid Enhancement for E3SM (Final Technical Report)

During the first phase of the project (2018/05-2021/05), we proved the hypothesis that, without tuning, high vertical resolution improves the representation of unresolved, parameterized low altitude clouds such as stratocumulus clouds in global climate models. We evaluated the effectiveness of the newly developed computational framework, Framework for Improvement by Vertical Enhancement (FIVE) by implementing it into E3SM. FIVE aims to improve representation of the target cloud, low level cloud for this project, with use of a locally high vertical resolution for computation of selected processes. FIVE was assessed via direct comparison among E3SM, E3SM-FIVE, and satellite data as well as a newly developed ensemble artificial neural network methodology. We demonstrated that E3SM-FIVE remarkably improves marine stratocumulus representation. To advance the FIVE methodology, we took a first step toward the adaptive vertical grid in the FIVE method by modifying the E3SM single column model. For future application of FIVE to the global storm resolving model with a few km horizontal grid resolution, we developed the vertical-meridional two-dimensional (2D) Hadley circulation model, which allows one to run simulations with much higher vertical resolution and significantly longer duration than current three-dimensional global storm resolving model. During the second phase of the project (2021/05-2023/11; period for this technical report), we sought a way to appropriately transform FIVE to a regionalized version of FIVE, which allows one to select locations with/without FIVE. In parallel, we demonstrated that E3SM-FIVE with a 25-km mesh significantly reduced the bias associated with coastal stratocumulus. Furthermore, we showed that the same improvement was able to be obtained with the use of both the Regional Refined Model (RRM) targeted to one of the stratocumulus regions with 25-km resolution and FIVE. We explored a new implementation strategy of FIVE with our 2D Hadley circulation model. In parallel we applied the 2D Hadley circulation model to study the double intertropical convergence zone bias with storm resolving model resolution and aerosol-cloud interactions within the Hadley circulation with near large eddy simulation resolution. Last, we have been investigating Arctic mixed-phase stratocumulus clouds with large eddy simulations in order to improve their representation in global models. During the course of the entire project, 6 journal articles were published, and 3 manuscripts are currently in preparation. We gave a total of 30 presentations at various domestic and international conferences. In addition we gave 6 seminar talks at universities and a national laboratory. E3SM- FIVE is available as a branch of E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Detecting Magnetic Fields in Exoplanets with Spectropolarimetry of the Helium Line at 1083 nm

The magnetic fields of the solar system’s planets provide valuable insights into their interiors and can have dramatic consequences for the evolution of their atmospheres and interaction with the solar wind. However, we have little direct knowledge of magnetic fields in exoplanets. Here we present a method for detecting magnetic fields in the atmospheres of close-in exoplanets based on spectropolarimetric transit observations at the wavelength of the helium line at 1083 nm. This methodology has been successfully applied for exploring magnetic fields in solar coronal filaments. Strong absorption signatures (transit depths on the order of a few percent) in the 1083 nm line have recently been observed for several close-in exoplanets. We show that in the conditions in these escaping atmospheres, metastable helium atoms should be optically pumped by the starlight and, for field strengths more than a few × 10 -4 G, should align with the magnetic field. This results in linearly polarized absorption at 1083 nm that traces the field direction (the Hanle effect), which we explore by both analytic computation and the HAZEL numerical code. The linear polarization $\sqrt{{Q}^{2}+{U}^{2}}/I$ ranges from ~10 -3 in optimistic cases down to a few × 10 -5 for particularly unfavorable cases, with very weak dependence on field strength. The line-of-sight component of the field results in a slight circular polarization (the Zeeman effect), also reaching $V/I\sim \mathrm{few}\times {10}^{-5}({B}_{\parallel }/10\,{\rm{G}})$. We discuss the detectability of these signals with current (SPIRou) and future (Extremely Large Telescope) high-resolution infrared spectropolarimeters, and we briefly comment on possible sources of astrophysical contamination.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Neutron transport methods for multiphysics heterogeneous reactor core simulation in Griffin

Griffin is a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). This work discloses the methods, algorithms, and implementation for simulating heterogeneous reactor dynamics models. Griffin utilizes a discontinuous finite-element method with discrete ordinates (DFEM-S ) to discretize the field variable of the multigroup neutron transport equation. Multiphysics feedback is handled using two-step tabulated cross-section methodology. Feedback quantities are evaluated using the MOOSE-MultiApp system to couple various engineering phenomena, such as heat conduction and thermal fluids. The multiphysics DFEM-S system is solved using fixed-point iteration with a fully asynchronous parallel sweeper, unstructured coarse-mesh finite difference acceleration, and a multi-timescale improved quasi-static method scheme. The implementation is applied to a multiphysics microreactor model, with two transients: one initiated by a single heat-pipe failure and another by control drum rotation. Importantly, these examples demonstrate the ability of Griffin to tractably solve the neutron transport equation considering seven independent variables and feedback.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tool and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the- art methods.

Guo, Luanzheng [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

MultiPEM Toolbox: User Manual [Rev. 2]

This document explains use of the Multi-Phenomenology Explosion Monitoring (Multi PEM) Toolbox, a collection of R scripts for estimating the unknown device parameters of a new event with uncertainty quantification. The methodology and application used for illustration in this user manual are fully documented in a Los Alamos National Laboratory technical report hereafter designated “WPA” for reference. Additional details on the application are found in a recent journal article. Two assessment types are available: rapid and complete. Rapid assessments are conducted in two stages, as described in Section 2. In the first stage, calibration data are used to estimate forward and error model parameters (WPA, §5.1) and (if relevant) errors-in-variables yield values for calibration sources (WPA, §3, Equation (3)). In the second stage, new event data are used to estimate the unknown new event device parameters (WPA, §5.2) with uncertainty quantification. Two options for treating the inferred first stage parameters in second stage Bayesian analysis are available: fixing them at their maximum likelihood estimate (default), or multiple imputation. Multiple imputation involves utilizing several posterior samples (imputations) of the first stage parameters as fixed values in the second stage posterior sampling of the new event device parameters. Second stage sampling is conducted across imputations in parallel to improve computational efficiency. This method produces improved uncertainty quantification of the new event device parameters compared with the default treatment of the first stage parameters, at the expense of additional computation. Complete assessments are conducted in a single stage, as described in Section 3. Calibration and (if relevant) new event data are used simultaneously to estimate all forward model, error model, and (if relevant) new event device parameters with uncertainty quantification on the latter. As the name suggests, rapid assessments generally run substantially faster than complete assessments (even with multiple imputation), because the results of first stage analysis can be stored and incorporated into estimating a relatively low-dimensional space of new event device parameters whenever relevant new event data becomes available. On the other hand, complete assessments must be run on the full set of model and device parameters with calibration and new event data every time the latter becomes available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PyJMAK: An Open-Source Python Toolkit for Modeling Solid-State Metallurgical Phase Transformations

Accurate prediction of metallurgical phase transformations is an essential basis for autonomous optimization and rapid part qualification. Several methods can be used to estimate the evolution of phase fractions such as JMAK kinetics-based models, phase-field models, thermodynamic models, and data-driven machine learning models. Thermodynamic and phase-field-based methodologies solve multiphysics equations requiring numerous calibration parameters and significant computational resources. As a result, the computation domain is limited to a point or on order of micron-meters. The data-driven models rely on large datasets from experiments and simulations. While the JMAK model only provides information about phase fraction evolution, it can predict this evolution in near real-time using thermal history and thermodynamic data without restriction on the domain. JMAK models have been popularly used by researchers to model phase transformations occuring during additive manufacturing or over arbitrary temperature profiles. Commercial proprietary software such as Abaqus and Ansys or closed-source in-house implementations offer the ability to model JMAK based kinetics to predict phase transformation. However, these software packages are not open-source or freely available for use and development in conjunction with manufacturing machines, sensors, and machine learning algorithms. In addition, the use of the model is restricted by a license token. In contrast, given temperature profiles at multiple points in the domain, this Python-based PyJMAK model can compute phase evolution in parallel due to its stand-alone modular, voxel-based structure, and it can be executed on high-performance computing resources without any license restrictions.

Prabhune, Bhagya [Oak Ridge National Laboratory (O↗
DOE OSTI2026

Performance-Aligned LLMs for Generating Fast HPC Code

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. Here, we demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP parallel code.

Computer science↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

kynema-sgf [SWR-20-85]

Kynema-SGF (formerly AMR-Wind), wherein SGF stands for structured-grid fluid dynamics, is a massively parallel, block-structured adaptive-mesh, incompressible flow solver. The codebase was initiated in 2019 from incflo. The solver is built on top of the AMReX library. AMReX library provides the mesh data structures, mesh adaptivity, as well as the linear solvers used for solving the governing equations. Kynema-SGF is actively developed and maintained by a dedicated multi-institutional team from Lawrence Berkeley National Laboratory, National Laboratory of the Rockies, and Sandia National Laboratories. The primary applications for Kynema-SGF are: performing large-eddy simulations (LES) of atmospheric boundary layer (ABL) flows, simulating wind farm turbine-wake interactions using actuator disk or actuator line models for turbines, and as a background solver when coupled with a near-body solver (e.g., Kynema-UGF) with overset methodology to perform blade-resolved simulations of multiple wind turbines within a wind farm. For offshore applications, the ability to model the air-sea interaction effects and its impact on the ABL characteristics is another focus for the code development effort. As with other codes in the Kynema ecosystem, Kynema-SGF shares the following objectives: *an open, well-documented implementation of the state-of-the-art computational models for modeling wind farm flow physics at various fidelities that are backed by a comprehensive verification and validation (V&V) process; *be capable of performing the highest-fidelity simulations of flow fields within wind farms; and *be able to leverage the high-performance leadership class computing facilities available at DOE national laboratories.

Ananthan, Shreyas↗
DOE OSTI2024

Enhanced PDV waveform search and analysis method using parallel circular-convolution / cross-correlation for improved dynamic surface velocity extraction [Poster]

Previous work on exhaustive search methodologies for extracting best-match parameters pertaining to dynamic surface quantities from PDV was done by cross-correlating synthetically generated PDV waveforms with observed counterparts using the circular-convolution theorem. This work was further developed into an open-source PDV analysis toolkit called CCPDVANALYSIS which expands upon and enhances the previously tested methods by parallelizing serial algorithmic components and incorporating a comprehensive script library for different flavors of instantaneous frequency functions utilized in generating synthetic PDV waveforms. Results of these enhancements have been shown to markedly decrease execution times of exhaustive search and extraction algorithms and produce improved velocity recoveries for low-velocity and dynamically varying velocity signals. The CCPDVANALYSIS script library demonstrates an advanced method for extracting velocities from low-velocity and non-constant velocity signals further extending and improving the methods beyond capabilities of traditional frequency domain tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Porting Classical Approaches for Quantum Simulations to Quantum Computers

Simulating quantum many-body systems is one of the most promising problems in which we might anticipate that quantum computers should show quantum advantage. Unfortunately, there is still a gap between this promise and actual practice. New quantum algorithms need to be developed and the current quantum algorithms have various difficulties - e.g efficient state preparation - which must be overcome and improved upon. In many cases, classical approaches need to be ported over to quantum devices. In this project we have developed a suite of new quantum algorithms which makes progress in this regard. We developed a new optimization scheme for variational quantum eigensolvers, UBOS, which mitigates problems with local minimas and barren plateaus while improving convergence to the ground state by an order of magnitude. We developed a new way to utilize qubitization to find ground states of nearly frustration-free Hamiltonians faster than all previous methods. We developed a series of state preparation techniques which helps initialize parameterized quantum circuits into reasonable starting points on which quantum algorithms are then applied. In addition to the development of novel algorithms, it is critical to have classical simulation techniques for approximately simulating quantum circuits which can be used to benchmark and understand quantum algorithms. Toward that end, we developed a novel POVM formalism to simulate quantum circuits as well as exemplify the massive parallelization of tensor network methodologies. Finally, we developed physical understanding of entanglement phase transitions such as many-body localization and random tensor networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Approach for energy efficient building design during early phase of design process

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Design Methodologies for Integrated Quantum Frequency Processors

We report frequency-encoded quantum information offers intriguing opportunities for quantum communications and networking, with the quantum frequency processor paradigm—based on electro-optic phase modulators and Fourier-transform pulse shapers—providing a path for scalable construction of quantum gates. Yet all experimental demonstrations to date have relied on discrete fiber-optic components that occupy significant physical space and impart appreciable loss. In this article, we introduce a model for the design of quantum frequency processors comprising microring resonator-based pulse shapers and integrated phase modulators. We estimate the performance of single and parallel frequency-bin Hadamard gates, finding high fidelity values that extend to frequency bins with relatively wide bandwidths. By incorporating multi-order filter designs as well, we explore the limits of tight frequency spacings, a regime extremely difficult to obtain in bulk optics. Overall, our model is general, simple to use, and extendable to other material platforms, providing a much-needed design tool for future frequency processors in integrated photonics.

97 MATHEMATICS AND COMPUTING↗