Search NASA⌕ Search

SEARCH · Search NASA

Results for “Simulation optimization”

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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Numerical aerodynamic simulation facility. Preliminary study extension

The production of an optimized design of key elements of the candidate facility was the primary objective of this report. This was accomplished by effort in the following tasks: (1) to further develop, optimize and describe the function description of the custom hardware; (2) to delineate trade off areas between performance, reliability, availability, serviceability, and programmability; (3) to develop metrics and models for validation of the candidate systems performance; (4) to conduct a functional simulation of the system design; (5) to perform a reliability analysis of the system design; and (6) to develop the software specifications to include a user level high level programming language, a correspondence between the programming language and instruction set and outline the operation system requirements.

Source record↗
DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Demonstration of Light-Augmented Calorimetry For Low-Energy Electrons in Liquid Argon.

The ability to resolve electrons with energies as low as 5 MeV in liquid argon (LAr) is important for future studies of astrophysical neutrinos in liquid argon time projection chambers (LArTPCs). Existing methods for measuring the energy of electromagnetic showers in LArTPCs rely solely on charge-based reconstruction which requires correcting for assumed electron-ion recombination. However, LAr scintillation light has the potential to improve energy reconstruction over charge-based measurements alone. Here we present a demonstration of a light-augmented calorimetric technique for low-energy electrons in LAr that foregoes the need to correct for recombination. A sample of Michel electrons from stopping cosmic muons is collected in LArIAT (Liquid Argon In A Testbeam), a single-phase LArTPC at Fermilab’s Test Beam Facility. Michel electron energy spectra are reconstructed using both a traditional charge-based approach as well as a more holistic approach that incorporates both charge and light. A maximum-likelihood fitter, using LArIAT’s well-tuned simulation, is developed for combining these quantities to achieve optimal energy resolution. A sample of isolated electrons with Ee < 60 MeV is then simulated to better determine the energy resolution of this topology. Due to LArIAT’s low wire noise, the addition of light is not found to significantly improve upon the achieved charged-based resolution of σ/E ≃ 9%/√E ⊕ 1%. However, additional samples are generated with varying wire noise levels and light yields to gauge the impact of light-augmented calorimetry in large LArTPCs such as DUNE and SBND. At a charge-readout signal-to-noise of ≈10, the energy resolution for low-energy electrons is improved by ∼15%, ∼25%, and ∼45% over charge-only calorimetry for average light yields of 10, 20, and 100 pe/MeV, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1974

HILTOP supplement: Heliocentric interplanetary low thrust trajectory optimization program, supplement 1

Modifications and improvements are described that were made to the HILTOP electric propulsion trajectory optimization computer program during calendar years 1973 and 1974. New program features include the simulation of power degradation, housekeeping power, launch asymptote declination optimization, and powered and unpowered ballistic multiple swingby missions with an optional deep space burn.

Mann, F. I.↗
NASA NTRS2018

High-Fidelity Multidisciplinary Design Optimization Methodology with Application to Rotor Blades

A multidisciplinary design optimization procedure has been developed and applied to rotorcraft simulations involving tightly-coupled, high-fidelity computational fluid dynamics and comprehensive analysis. A discretely-consistent, adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows on unstructured, dynamic, overset meshes, while a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system for high-fidelity rotorcraft analysis is verified; simulation results exhibit good agreement with established solutions. A constrained gradient-based design optimization for a HART-II rotorcraft configuration is demonstrated. The computational cost for individual components of the multidisciplinary sensitivity analysis is assessed and improved.

Wang, Li↗
NASA NTRS2020

Path-Adaptive Guidance Algorithm Trades for a Two-Stage Lunar Descent Vehicle

For the next generation of NASA’s missions, the necessity for path-adaptive guidance algorithms has become clear in order to provide the stability and customizability required for a safe and efficient descent to the lunar surface, while meeting specified program and vehicle constraints. Several descent algorithms have been tested and flown for single-stage landers through the Apollo and Altair programs, but thus far little analysis has been conducted involving the application of these algorithms for a two-stage descent vehicle. Due to the limited payload mass constraints of the existing fleet of launch vehicles, multi-stage descent architectures have become a viable course of action. This paper seeks to compare the performance of guidance configurations for a lunar lander system consisting of two stages, one of which separates partway through descent. Through development of this paper, an optimization suite has been written that is specifically designed for optimizing planetary non-atmospheric two-stage descent trajectories, and is used as a comparison baseline for the guidance algorithms tested. Time-to -go computational methods and ignition logic routines that may be employed in a lunar environment are also discussed. Further work is to be completed on trajectory design trades as well as the effects of modifying guidance targets in simulation based on trajectories that are optimized for different performance indices.

Jason M Everett↗
DOE OSTI2026

Interface and Thermophysical Properties of R 32 Refrigerant

Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH 2 F 2 ) has emerged as a promising candidate owing to its favorable thermophysical properties, including high heat transfer efficiency and low viscosity. While bulk properties such as density, viscosity, and thermal conductivity have been widely studied, interfacial properties, including surface tension and interfacial thickness, remain comparatively underexplored, despite their importance in phase-transition dynamics. Here, we perform molecular dynamics (MD) simulations from 180 to 300 K using an optimized transferable force field for fluoropropenes with enhanced electrostatics to assess both bulk and interfacial behavior of R32. Simulations reproduced density within ±2.1%, viscosity within 3.05%, and thermal conductivity within 7.41% of NIST reference data. Heat capacities (C p and C v ) were predicted within 5%. For interfacial properties, surface tension trends were reproduced within 13.58% deviation, and the vapor–liquid coexistence curve closely matched reference data, yielding a critical temperature of 345.7 K (1.6% deviation) and a critical density of 0.397 g/cm 3 (6.4% deviation). Importantly, the vapor–liquid interface exhibited pronounced temperature-dependent broadening across the 180–290 K range. This behavior correlates with increasing molecular kinetic energy, reduction in intermolecular cohesive interactions, and a progressive loss of preferential dipole alignment, which collectively enhance thermal fluctuation amplitudes at elevated temperatures. These validated results provide predictive molecular-level insights, particularly for interfacial properties that remain less characterized. By reducing property prediction errors in key parameters such as critical temperature, this work provides reliable inputs for heat-exchanger and system models. Such correlations can support optimized component sizing, improved performance, and reduced refrigerant charge. Beyond R32, the methodology offers a transferable framework for blended and next-generation low-GWP refrigerants, contributing to sustainable thermal management aligned with the 2027 EU F-Gas regulation and 2030 Kigali Amendment.

Fluids↗
DOE OSTI2020

AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case

Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. Here, we propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as a potential component of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our technique can be applied to any detector R&D, provided that realistic simulations are available.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods

Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.

MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Optimal helicopter trajectory planning for terrain following flight

Helicopters operating in high threat areas have to fly close to the earth surface to minimize the risk of being detected by the adversaries. Techniques are presented for low altitude helicopter trajectory planning. These methods are based on optimal control theory and appear to be implementable onboard in realtime. Second order necessary conditions are obtained to provide a criterion for finding the optimal trajectory when more than one extremal passes through a given point. A second trajectory planning method incorporating a quadratic performance index is also discussed. Trajectory planning problem is formulated as a differential game. The objective is to synthesize optimal trajectories in the presence of an actively maneuvering adversary. Numerical methods for obtaining solutions to these problems are outlined. As an alternative to numerical method, feedback linearizing transformations are combined with the linear quadratic game results to synthesize explicit nonlinear feedback strategies for helicopter pursuit-evasion. Some of the trajectories generated from this research are evaluated on a six-degree-of-freedom helicopter simulation incorporating an advanced autopilot. The optimal trajectory planning methods presented are also useful for autonomous land vehicle guidance.

Menon, P. K. A.↗
NASA NTRSDate not supplied

Numerical Investigation of Flow Over Periodic Hill

Hybrid turbulence modeling strategies that blend between large-eddy simulation and Reynolds-averaged Navier-Stokes promise to make optimal use of the available grid resolution. A delayed detached eddy simulation hybrid turbulence modeling strategy, based on the shear-stress transport turbulence model, was implemented into a finite-volume compressible Navier-Stokes code. Then the flow through a two-dimensional periodic hill channel was simulated. This geometry is a widely accepted benchmark test case for validating hybrid turbulence modeling strategies. The computational grid for the simulation was downloaded from a hybrid turbulence model validation website. The present hybrid turbulence model simulation predicts the size of the separated flow region as well as velocity and Reynolds stress profiles in good agreement with large-eddy simulation reference data.

Computational Fluid Dynamics↗
DOE OSTI2019

The Gas-Jet Method of Deposition of Nanostructured Silver Films

The synthesis of thin silver films by the gas-jet deposition method is experimentally and theoretically studied. When the metal is deposited onto silicon substrates from a supersonic jet of silver vapor with a helium carrier gas, nanostructured films with a 3−30 nm size of nanostructures are obtained for a 1230−1380 K range of jet source temperatures. The data on Ag–He gas-jet dynamics when it is expanded into vacuum (velocity, temperature, concentration, flux of particles onto a substrate) depending on parameters at the source (vapor temperature, flow rate of a carrier gas) are obtained by the method of direct simulation Monte Carlo. The range of optimal helium flow rates, when the efficiency of a gas-jet source is maximal, is determined. It is established that the presence of a background gas in a deposition chamber at pressure higher than 1 Pa decreases the flow of particles onto a substrate, and a simple way of its evaluation is proposed. Conditions for formation of silver clusters in the jet are determined by using the simulation. It is shown that for experimental deposition regimes there are no clusters in the jet, and the observed silver nanostructures are formed on the substrate surface.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem—solution of the underlying mathematical model to yield output quantities of interest, given input parameters—is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal configuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems—requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models—entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure.

58 GEOSCIENCES↗
DOE OSTI2024

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗
DOE OSTI2023

FAST Low-Energy Beamline Studies: Toward High-Peak 5D Brightness Beams for FAST-GREENS

The FAST beamline is the injector for the planned Gamma-Ray Electron ENhanced Source (GREENS) program, which aims to achieve the demonstration and first application of a high-efficiency, high-average-power free-electron laser at 515 nm. FAST-GREENS requires high 5D peak brightness; transverse normalized projected emittances of 3 mm-mrad and a peak current of 600 A are the minimum beam requirements for the FEL to reach the 10% efficiency goal. In this work, studies of the low-energy section of the FAST beamline are presented toward these ends, including preliminary measurements of beam compression and beam emittance. An effort toward developing a high-fidelity simulation model that could be later optimized for FAST-GREENS is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Wang, Z↗
DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗

Refine your search to explore more results.