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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation

Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more domain-invariant features. Domain adaptation (DA) methods include a range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SIDDA, an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, and real astronomical observations. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with equivariant neural networks (ENNs). We find that SIDDA enhances the generalization capabilities of NNs, achieving up to a ≈40% improvement in classification accuracy on unlabeled target data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, we find that SIDDA enhances model calibration on both source and target data--achieving over an order of magnitude improvement in the ECE and Brier score. SIDDA's versatility, combined with its automated approach to domain alignment, has the potential to advance multi-dataset studies by enabling the development of highly generalizable models.

Pandya, Sneh [Northeastern U.]↗
DOE OSTI2021

Principal factors in performance of indirect-drive laser fusion experiments

Progress in inertial confinement fusion depends on the accurate interpretation of experiments that are complex and difficult to explain with simulations. Results could depend on small changes in the laser pulse or target or physics that are not fully understood or characterized. In this paper we discuss an x-ray-driven platform [K. Baker et al., Phys. Rev. Lett. 121, 135001 (2018)] with fewer sources of degradation, and find the fusion yield can be described as a physically motivated function of laser energy, target scale, and implosion symmetry. This platform and analysis could enable a more experimental approach to the study and optimization of implosion physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Enabling Scale-Up Through Multi-Fidelity Adaptive Computing

We present ideas from our ongoing work in adaptive computing - an optimization framework that allows us to strategically deploy various fidelity level experiments and simulations to guide decision making. The framework aims to enable uncertainty quantified scale-up of simulations and experiments, which causes increased complexity. A key feature of the framework is the integration of user-specified local model trustworthiness estimates. Adaptive sampling strategies allow us to optimally exploit the multiple fidelity level information and trustworthiness measures to arrive at the best decisions within a highly limited budget of objective function evaluations.

adaptive sampling↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Phase behavior of continuous-space systems: A supervised machine learning approach

The phase behavior of complex fluids is a challenging problem for molecular simulations. Supervised machine learning (ML) methods have shown potential for identifying the phase boundaries of lattice models. In this work, we extend these ML methods to continuous-space systems. We propose a convolutional neural network model that utilizes grid-interpolated coordinates of molecules as input data of ML and optimizes the search for phase transitions with different filter sizes. We test the method for the phase diagram of two off-lattice models, namely, the Widom–Rowlinson model and a symmetric freely jointed polymer blend, for which results are available from standard molecular simulations techniques. The ML results show good agreement with results of previous simulation studies with the added advantage that there is no critical slowing down. We find that understanding intermediate structures near a phase transition and including them in the training set is important to obtain the phase boundary near the critical point. The method is quite general and easy to implement and could find wide application to study the phase behavior of complex fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Thermal Performance of Spandrel Assemblies in Glazed Wall Systems: Laboratory Test Design – Challenges and Test Results

Accurate thermal performance calculation procedures for opaque spandrel areas in curtain wall and window wall systems are essential for rating systems when comparing spandrel systems. However, there is a lack of consensus in thermal modeling needed for accurately characterizing heat transfer through spandrel assemblies due to the complex arrangement of materials and structural components. Several studies indicate that conventional 2D thermal simulations may overestimate R-values by 30% compared to physical testing and 3D simulations. Detailed simulations and well-curated laboratory test data are necessary to build confidence in simulation models, which will later be used to develop correlations to improve widely used conventional 2D thermal simulations. This study aims to experimentally test heat transfer through various spandrel assemblies to validate 3D simulation models. Also, the challenges of conducting a thorough testing design along with the solutions would be documented. The team developed a design for testing spandrel assemblies, making appropriate modifications to the existing heat, air, and moisture (HAM) chamber to accommodate the testing needs. Two moveable baffles were designed and fabricated to guide airflow direction parallel to the test article surface. The data acquisition capabilities in the chamber were upgraded to add more than two hundred sensors to the climate and indoor side of the chamber. The goal is to provide a quality dataset for validating complex 3D modeling simulations, which will be used to develop improved thermal simulation techniques that more accurately represent the thermal behavior of spandrel assemblies and their integration within the building envelope. This paper will summarize the results for the boundary conditions of the testing and the temperature variation across different locations of the spandrel assemblies.

Kunwar, Niraj [ORNL] (ORCID:0000000263457652)↗
DOE OSTI2024

Influences of the South American Low-Level Jet on the Convective Environment in Central Argentina Using a Convection-Permitting Simulation

This study documents the spatial and temporal distribution of the South American low-level jet (SALLJ) and quantifies its impact on the convective environment using a 6.5-month convection-permitting simulation during the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) campaigns. Overall, the simulation reproduces the observed SALLJ characteristics in central Argentina near the Sierras de Córdoba (SDC), a focal point for terrain-focused upscale growth. SALLJs most frequently occur in the summer with maxima to the northwest and east of the SDC and minima over the higher terrain. The shallower SALLJs (<1750 m) have a strong overnight skew, while the elevated jets are more equally spread throughout the day. SALLJ periods often have higher amounts of low-level moisture and instability compared to non-SALLJ periods, with these impacts increasing over time when the SALLJ is present and decreasing afterward. The SALLJ may enhance low-level wind shear magnitudes (particularly when accounting for the jet height); however, enhancement is somewhat limited due to the presence of speed shear in most situations. SALLJ periods are associated with low-level directional shear favorable for organized convection and an orientation of cloud-layer wind shear parallel to the terrain, which could favor upscale growth. A case study is shown in which the SALLJ influenced both the magnitude and direction of wind shear concurrent with convective upscale growth near the SDC. This study highlights the complex relationship between the SALLJ and its impacts during periods of widespread convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Real-time Implementation of a Dual-Active-Bridge Based Multi-Level Photovoltaic Converter

Silicon-carbide based, multi-level converters with dual-active-bridge (DAB) are getting attention for medium voltage, direct grid-connected, utility-scale photovoltaic (PV) applications due to their higher efficiency and power density. To de-risk complex control development for these high-voltage and high-power inverters, hardware-in-the-loop techniques are often required. Due to the high switching frequency and the large number of power electronic switches, off-the-shelf, real-time simulation tools are not yet fully developed for this application, even with FPGA-based simulators. To address this, a new co-simulation approach in FPGA is presented that utilizes Time-Stamped Bridge (TSB) for the higher frequency converters along with detailed fixed conductance based discrete device simulation of the rest of the circuit. A complex DAB-based PV converter with maximum switching frequency of 50 kHz is successfully simulated in real-time in the FPGA and the results are compared with offline simulation results. A controller hardware-in-the-loop (CHIL) platform is developed to demonstrate real-time, closed-loop test results for this DAB-based PV converter.

Dong, Zerui↗
DOE OSTI2024

Solving the Hele–Shaw flow using the Harrow–Hassidim–Lloyd algorithm on superconducting devices: A study of efficiency and challenges

The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid dynamics. In this study, we evaluate the Harrow–Hassidim–Lloyd (HHL) quantum linear systems algorithm (QLSA) for solving the idealized Hele–Shaw flow. Our focus is on the accuracy and computational cost of the HHL solver, which we find to be sensitive to the condition number, scaling exponentially with problem size. This emphasizes the need for preconditioning to enhance the practical use of QLSAs in fluid flow applications. Moreover, we perform shots-based simulations on quantum simulators and test the HHL solver on superconducting quantum devices, where noise, large circuit depths, and gate errors limit performance. Error suppression and mitigation techniques improve accuracy, suggesting that such fluid flow problems can benchmark noise mitigation efforts. Finally, our findings provide a foundation for future, more complex application of QLSAs in fluid flow simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Mismatch losses in a PV system due to shortened strings

Numerous events may require intentional removal of one or more photovoltaic modules from a string, shortening the length of the string relative to others within the array, resulting in a string length mismatch. The impact of such a mismatch is not well understood either in measurable operational effects (voltage, current, power) or in the potential effects on long-term module health. It is impractical to solely approach this problem experimentally due to the size and complexity of arrays that may experience string length shortening. Here this work presents simulations, validated through limited field experiments on a two string array, providing a basis from which more complex arrays and scenarios may be explored. Refinement of the simulation achieves an overall error in IMPP for the nominal (S 1 ) and test strings (S 2 ) between the simulation and experimental values, through all test conditions, of +0.35 ± 1.46% and -0.36 ± 1.58% respectively. Shortening one of two strings by one module results in a power loss greater than the power contribution of the module alone (1.29 module equivalents); the impact increases through the maximum test case of a six module mismatch with a power loss equivalent to more than 11 modules. The impact of using string-end blocking diodes is presented with an emphasis at the array maximum power point and at open circuit. Implications are discussed for arrays of higher complexity.

14 SOLAR ENERGY↗
DOE OSTI2025

Delocalized Excitation Transfer in Open Quantum Systems with Long-Range Interactions

The interplay between coherence and system-environment interactions is at the basis of a wide range of phenomena, from quantum information processing to charge and energy transfer in molecular systems, biomolecules, and photochemical materials. In this work, we use a Frenkel exciton model with long-range interacting qubits coupled to a damped collective bosonic mode to investigate vibrationally assisted transfer processes in donor-acceptor systems featuring internal substructures analogous to light-harvesting complexes. We find that certain delocalized excitonic states maximize the transfer rate and that the entanglement is preserved during the dissipative transfer over a wide range of parameters. We investigate the reduction in transfer caused by static disorder, white noise, and finite temperature and study how transfer efficiency scales as a function of the number of dimerized monomers and the component number of each monomer, finding which excitonic states lead to optimal transfer. Finally, we provide a realistic experimental setting to realize this model in analog trapped-ion quantum simulators. Analog quantum simulation of systems comprising many and increasingly complex monomers could offer valuable insights into the design of light-harvesting materials, particularly in the nonperturbative intermediate parameter regime examined in this study, where classical simulation methods are resource intensive.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Digital Quantum Simulation of the Schwinger Model and Symmetry Protection with Trapped Ions

Tracking the dynamics of physical systems in real time is a prime application of quantum computers. Using a trapped-ion system with up to six qubits, we simulate the real-time dynamics of a lattice gauge theory in 1+1 dimensions, i.e., the lattice Schwinger model, and demonstrate nonperturbative effects such as pair creation for times much longer than previously accessible. We study the gate requirement of two formulations of the model using the Suzuki-Trotter product formula, as well as the trade-off between errors from the ordering of the Hamiltonian terms, the Trotter step size, and experimental imperfections. To mitigate experimental errors, a recent symmetry-protection protocol for suppressing coherent errors and a symmetry-inspired postselection scheme are applied. This work demonstrates the integrated theoretical, algorithmic, and experimental approach that is essential for efficient simulation of lattice gauge theories and other complex physical systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Detector and Beamline Simulation for Next-Generation High Energy Physics Experiments

The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dynamics require sophisticated techniques in order to meet the demands of current and future experiments. Common software tools used today are unable to fully utilize modern computational resources, while data-recording rates are often orders of magnitude larger than what can be produced via simulation. In this paper, we describe the state, current and future needs of high energy physics detector and beamline simulations and related challenges, and we propose a number of possible ways to address them.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗
DOE OSTI2025

The NASA ACTIVATE Mission

The NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) conducted 162 joint flights with two aircraft over the northwest Atlantic to study aerosol–cloud interactions (ACIs), which represent the largest uncertainty in estimating total anthropogenic radiative forcing. The combination of a high-flying King Air and low-flying HU-25 Falcon, equipped with remote sensing and in situ instruments, characterized trace gases, aerosol particles, clouds, and meteorological variables with data collected nearly simultaneously below, within, and above marine boundary layer (MBL) clouds. Flights spanning warm and cold seasons across 3 years (2020–22) provided a broad range of conditions associated with aerosol particles, cloud properties (including particle size and phase), and meteorology, ideally suited for robust ACI calculations and assessing how well models simulate a wide range of MBL clouds from stratiform to cumulus. ACTIVATE data suggest that drivers of cloud droplet number concentration N d , including aerosol particles and MBL dynamics, vary between winter and summer months with a stronger potential to convert aerosol particles into cloud droplets in winter. Models of varying complexity not only highlight some skills in simulating winter and summer cloud types but also identify challenges that still need to be addressed such as treatment of turbulence, wet scavenging, and mesoscale organization. Remote sensing advances range from new retrieval methods for N d , cloud phase classification, vertically resolved aerosol and cloud condensation nuclei number concentration, and ocean surface wind speed. This work describes these scientific and technological advances along with efforts in outreach and open data science.

aerosol indirect effect↗