Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “complex simulations”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Electromagnetic Transient Simulation Algorithms for Evaluation of Large-Scale Extreme Fast Charging Systems (Distribution Grid Models)

The distribution and transmission grids are observing an increased penetration of power electronics in loads and generations. For example, there is increasing interest in integrating in extreme fast charging (XFC) systems for fast charging of electrical vehicles. As these systems are integrated, developing high-fidelity electromagnetic transient model of XFC systems in distribution grids and evaluating their interactions with the power grid would be of significant interest. This model will be utilized for design of XFC systems, to identify upgrades in distribution and/or transmission grids, for planning purposes by transmission planners or operators or owners, among others. It can also be utilized in operations for improved reliable performance of the grid and/or XFC station. The challenge with simulating these models is the high computational complexity introduced by the large number of states present in the system and the time-step needed to simulate the system. In this paper, advanced simulations algorithms are applied to reduce the computational complexity of simulating large-scale XFC systems. The algorithms include numerical stiffness-based segregation, time constant-based segregation, clustering and aggregation on differential algebraic equations (DAEs), and multi-order integration approaches. While the first three algorithms split the matrix that needs to be inverted from a large matrix to much smaller matrices, the final algorithm reduces the computational burden of applying higher-order integration approaches in the complete system. The comparison made in the previous sentence is with respect to use of homogeneous integration approaches used in conventional electromagnetic transient simulators like power systems computer aided design (PSCAD). The approaches mentioned here have resulted in speed-up of 36x in the simulation of a single distribution system with 15 XFCs.

Debnath, Suman↗
DOE OSTI2022

Hybrid quantum-classical approach for coupled-cluster Green's function theory

The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of time evolution and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system's eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green's function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to many models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grows as O ( N 5 ) per time step to calculate the impurity Green's function in the time domain, where N is the total number of energy levels in the AIM. Since the number of T gates is analogous to the computational time complexity of a classical simulation, we achieve an order of magnitude improvement over a classical CCGF calculation of the same order, which requires O ( N 6 ) computational resources per time step.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Computational alchemy clarifies origins of alloy strengthening

Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~10 8 atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.

36 MATERIALS SCIENCE↗
DOE OSTI2025

2024 Incubator Final Report: Active Hybrid Mooring

This Incubator project has defined and developed a representation of an active hybrid mooring (AHM) system in Simulink to assess whether the speed and accuracy of AHM is sufficient to warrant further investigation in a larger project. We first built a representative mooring system for the VolturnUS-S 15MW floating offshore wind turbine (FOWT) in 1,000-meter water depth to provide baseline loads and displacements needed in the AHM system, with the results showing off-the-shelf actuators would be practical. We then defined model configurations for the numerical and physical substructures, using standalone MoorDyn v2 with a novel input strategy and chained hydraulic actuators represented in Simulink Simscape, respectively. The remainder of the project has focused on verifying the existing models for speed and accuracy against OpenFAST simulations, increasing their complexity to enable six degree of freedom (6DOF) operation, and connecting the numerical and physical substructures into a single holistic model. The verification phase of the project has shown promising results, though further refinement is needed to resolve accuracy discrepancies and for robust performance across a wider range of metocean conditions. The numerical model we developed is computationally stable and operates faster than real time using a time step of 0.01 seconds or shorter, indicating the model is likely fast enough for our AHM design to operate successfully. Additionally, the Simscape model of a hydraulic actuator operates with less than 0.3% relative error when actuating mooring loads from an OpenFAST simulation of the VolturnUS-S 1,000-meter mooring system. The aforementined refinements will be performed in the next year as a Continuing Incubator, with key remaining tasks include programming the predictor-corrector loop to minimize movement error, testing against more complex nonlinear wave cases, and expanding to enable loads from connected shared mooring lines.

17 WIND ENERGY↗
DOE OSTI2022

Development of an Enhanced Radiation Physics Toolset for Modeling Spectral and Imaging Signatures in the Warm Dense Matter Experiments

Radiative and atomic processes in plasmas play a critical role in a wide variety of high energy density laboratory plasma (HEDLP) experiments. The emission, absorption, and transport of radiation can strongly affect the overall energetics and evolution of such plasmas. In addition, radiation-based diagnostics – including imaging, spectroscopy, and absolute flux measurements – are widely used to determine key features of HEDLPs. To advance our understanding of HEDLP science, it is vital to have high-fidelity computational physics tools that have well-tested radiation physics modeling, and that are readily accessible to researchers in the HEDLP community. Simulations play an extremely important role for planning and designing the experiments, as well as for post-experiment data analysis. Prism Computational Sciences develops software that is used by National Laboratories and universities (including five members of LaserNetUS network). Prominent examples of such research efforts include z-pinch and short-pulse laser experiments designed to study the basic physics of photoionized plasmas and photoionization fronts, as well as their application to astrophysical plasmas. The main effort was dedicated to the development of non-equilibrium equation-of-state (EOS) models within the HELIOS-CR code, a hydrodynamics code with inline collisional-radiative atomic kinetics. Gas cell experiments on Z and Omega demonstrated the importance of non-equilibrium effects on atomic kinetics in photoionized plasmas. Recent proof-of-principle experiments on Omega EP confirmed the advantages of using a short-pulse laser to create an intense radiation drive, leading to additional experiments being proposed. Photoionization front experiments at LLE also emphasizes the importance of radiation and atomic physics. In both studies, HELIOS-CR simulations played a crucial role in computing non-equilibrium opacities and ionization distributions. A newly developed non-LTE EOS model will help addressing possible non-equilibrium effects, on for example specific heat, and their influence in plasma evolution. Prism also implemented support for open-source atomic data generated by the Flexible Atomic Code. This allows researchers to generate custom atomic tables and use them within the complex framework of simulation tools developed by Prism. The ability to use open-source atomic data would be extremely valuable for hydrodynamics and spectroscopic simulations that include high-Z materials, e.g., picosecond x-ray pulse generation experiments. Support for new atomic structures was fully implemented, and the data can be used by all simulation tools developed at Prism: radiation-hydrodynamics, imaging and spectroscopy, EOS and opacity. The development resulted in a significant fidelity enhancement to the simulations tools developed by Prism that are currently used in other cutting-edge experiments including: opacity measurement experiments performed to both understand the basic radiative and atomic properties of plasmas as well as provide data for more accurately modeling the internal structure of the Sun and other stars, high-intensity short-pulse laser experiments performed to develop short-wavelength light sources for use as backlighters and to investigate fast ignition concepts for inertial fusion energy; capsule implosion experiments designed to develop inertial fusion as an energy source, etc.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Induced Generative Adversarial Particle Transformers

In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message-passing generative adversarial network (MPGAN) was the first model to simulate collisions as point, or ``particle'', clouds, with state-of-the-art results, but suffered from quadratic time complexity. Recently, generative adversarial particle transformers (GAPTs) were introduced to address this drawback; however, results did not surpass MPGAN. We introduce induced GAPT (iGAPT) which, by integrating ``induced particle-attention blocks'' and conditioning on global jet attributes, not only offers linear time complexity but is also able to capture intricate jet substructure, surpassing MPGAN in many metrics. Our experiments demonstrate the potential of iGAPT to simulate complex HEP data accurately and efficiently.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

Knapp, Gerald L. [Oak Ridge National Laboratory (O↗
DOE OSTI2025

Building confidence in models for complex barrier systems for radionuclides

The modeling and simulation of the Cement-clay Interaction-Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include "skin effects" developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.

Sarsenbayev, Dauren↗
DOE OSTI2021

MOSCATO Solver Development and Integration Plan

During FY21, we conducted ongoing development work for the MOSCATO (Molten Salt Chemistry and Transport) solver. The code development work primarily consisted of transitioning capabilities from the original version of the solver, which was written in OpenFOAM, into Nek5000. In doing so, a fast, highly parallelizable solver was created that is capable of complex chemistry and corrosion simulations for engineering-scale molten salt systems. The Nek5000 version of MOSCATO is now fully featured and capable of higher-fidelity simulations than were previously possible. Demonstration cases including a thermal convection loop have been simulated to test these new capabilities. Although capable of large-scale simulations, MOSCATO is not well-suited to parametric studies of complete reactor geometries. These types of simulations are instead better handled by reduced-order modeling codes such as ORNL’s Mole code. Reduced-order simulation tools like Mole, however, are dependent on high fidelity correlations to account for complex, coupled three-dimensional phenomena that they do not directly simulate. Tools such as MOSCATO must therefore be used to create these correlations, as suitable empirical relationships are not available for most molten salt systems. Toward that end, we used the Nek-derived version of MOSCATO to create new mass transfer correlations for three relevant cases including tubular, tube bank, and subchannel geometries. These new correlations are more accurate than any existing ones and can be readily integrated into any reduced-order modeling tools that are targeting full-scale MSR simulations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data

Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. Here, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feedforward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a metamodelling framework for building energy models with application to fifth-generation district heating and cooling networks

Fully defined physics-based building energy models can accurately represent building systems; however, generating models based on high-level parameters is time consuming and simulation time of complex models can be slow. This article discusses the development of a Metamodelling Framework to create metamodels from a building energy modelling dataset. The framework generates metamodels using either linear regression, random forests, or support vector regressions. A fifth-generation district heating and cooling system analysis use case was used to motivate the development of the framework. The use case required quick and accurate representations of annual building loads reported hourly. Typical annual building modelling approaches can result in a runtime of 10 min. The metamodels runtime was reduced to less than 10 s to load and run an annual simulation with user-defined covariates. The results of the metamodel performance and an abbreviated topology analysis based on the motivating use case will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Mathematical analysis of laboratory microbial experiments demonstrating deterministic chaotic dynamics

Presented is a system of four ordinary differential equations and a mathematical analysis of microbiological experiments in a four-component chemostat—nutrient n, rods r, cocci c, and predators p. The analysis is consistent with the conclusion that previous experiments produced features of deterministic chaotic and classical dynamics depending on dilution rate. The surrogate model incorporates as much experimental detail as possible, but necessarily contains unmeasured parameters. The objective is to understand better the differences between model simulations and experimental results in complex microbial populations. The key methodology for simulation of chaotic dynamics, consistent with the measured dilution rate and microbial volume averages, was to cause the preference of p for r vs. c to vary with the r and c concentrations, to make r more competitive for nutrient than c, and to recycle some dying p biomass, leading to a modified version of the Monod kinetics model. Our mathematical model demonstrated that the occurrence of chaotic dynamics requires a predator, p, preference for r versus c to increase significantly with increases in r and c populations. Also included is a discussion of several generalizations of the existing model and a possible involvement of the minimum energy dissipation principle. This principle appears fundamental to thermodynamic systems including living systems. Several new experiments are suggested.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Incorporating the molecular-scale into a hydrodynamic description of confined aqueous systems

Hydrodynamics provides a continuum-level description of fluid motion, but its applicability at the nanoscale becomes uncertain due to the emerging importance of molecular-level effects such as spatial heterogeneity. Hydrodynamic boundary conditions that incorporate molecular details allow us to partition the system into a near-wall region and a bulk fluid region. We identify a hydrodynamic wall located inside the fluid that determines where slip begins. By extending the hydrodynamic wall with the slip length, the position of the extrapolated wall is established. This offers a unified description of both slip and stagnant flow behaviors, with wall hydrophobicity characterized by the relative location of the extrapolated wall with respect to the physical wall. Employing this concept in analyses of equilibrium molecular dynamics (MD) and non-equilibrium MD simulations of Couette and Poiseuille flows, our results demonstrate consistency between equilibrium and non-equilibrium approaches across different flow types and confinement levels. This demonstrates the robust nature of linear response theory. We then explore the effects of fluid-wall and bulk fluid interactions on the hydrodynamic properties. These findings enhance the effectiveness of molecular-based simulations for investigating complex confined systems in nanofluidics, biology, and colloidal science, offering a complementary molecular-scale perspective to traditional continuum approaches.

flow simulations↗
DOE OSTI2024

Quasilinear modelling of collisional trapped electron modes

The simplification of collision operators is necessary for quasilinear turbulence modelling used with integrated modelling frameworks, such as the gyrokinetic code QuaLiKiz. The treatment of collisions greatly impacts the accuracy of trapped electron mode (TEM) modelling, which is necessary to predict the electron heat flux and the balance between inward and outward particle fluxes. In particular, accurate particle flux predictions are necessary to successfully model density peaking in the tokamak core. We explored two ways of improving collisional TEM model reduction for tokamak core plasmas. First, we carried out linear GENE simulations to study the complex interplay between collisions and trapped electrons. We then used these simulations to define an effective trapped fraction to characterize the collisional TEM based on two key parameters, the local inverse-aspect ratio $\epsilon$ and the collisionality $\nu ^\ast$. One aspect missing from analytical TEM research is that the collisional frequency and the bounce-transit frequency are both velocity dependent; this effective trapped fraction takes both into account. In doing so, we determined that two parameters are not enough to model the collisional TEM, as an additional third free parameter was necessary. We determined that this model, as currently formulated, is not suitable for integrated modelling purposes. Second, we directly improved QuaLiKiz's Krook operator, which relies on two free parameters. We determined that these parameters required adjustments against higher-fidelity collisional models. In order to improve density profile predictions when paired with integrated models, we refined the Krook operator by using GENE simulations as a higher-fidelity point of comparison. We then demonstrate strong improvement of density peaking predictions of QuaLiKiz within the integrated modelling framework JETTO.

fusion plasma↗
DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗
DOE OSTI2022

Quantum simulation of real-space dynamics

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a d-dimensional Schrödinger equation with η particles can be simulated with gate complexity O ~ (ηdFpoly(log(g'/ϵ))), where ϵ is the discretization error, g' controls the higher-order derivatives of the wave function, and F measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on ϵ and g' from poly(g'/ϵ) to poly(log(g'/ϵ)) and polynomially improves the dependence on T and d, while maintaining best known performance with respect to η. For the case of Coulomb interactions, we give an algorithm using η 3 (d + η)Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates, and another using η 3 (4d) d/2 Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates and QRAM operations, where T is the evolution time and the parameter Δ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗