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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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At least 829 records · Page 46

All sources NASA NTRS DOE OSTI

NASA NTRS2015

The Effects of Sweeping Jet Actuator Parameters on Flow Separation Control

A parametric experimental study was performed with sweeping jet actuators (fluidic oscillators) to determine their effectiveness in controlling flow separation on an adverse pressure gradient ramp. Actuator parameters that were investigated include blowing coefficients, operation mode, pitch and spreading angles, streamwise location, aspect ratio, and scale. Surface pressure measurements and surface oil flow visualization were used to characterize the effects of these parameters on the actuator performance. 2D Particle Image Velocimetry measurements of the flow field over the ramp and hot-wire measurements of the actuator's jet flow were also obtained for selective cases. In addition, the sweeping jet actuators were compared to other well-known flow control techniques such as micro-vortex generators, steady blowing, and steady vortex-generating jets. The results confirm that the sweeping jet actuators are more effective than steady blowing and steady vortex-generating jets. The results also suggest that an actuator with a larger spreading angle placed closer to the location where the flow separates provides better performance. For the cases tested, an actuator with an aspect ratio, which is the width/depth of the actuator throat, of 2 was found to be optimal. For a fixed momentum coefficient, decreasing the aspect ratio to 1 produced weaker vortices while increasing the aspect ratio to 4 reduced coverage area. Although scaling down the actuator (based on the throat dimensions) from 0.25 inch x 0.125 inch to 0.15 inch x 0.075 inch resulted in similar flow control performance, scaling down the actuator further to 0.075 inch x 0.0375 inch reduced the actuator efficiency by reducing the coverage area and the amount of mixing in the near-wall region. The results of this study provide insight that can be used to design and select the optimal sweeping jet actuator configuration for flow control applications.

Koklu, Mehti↗

NASA NTRS2017

A Modified Triples Algorithm for Flush Air Data Systems that Allows a Variety of Pressure Port Configurations

Air Data Systems (FADS) are becoming more prevalent on re-entry vehicles, as evi- denced by the Mars Science Laboratory and the Orion Multipurpose Crew Vehicle. A FADS consists of flush-mounted pressure transducers located at various locations on the fore-body of a flight vehicle or the heat shield of a re-entry capsule. A pressure model converts the pressure readings into useful air data quantities. Two algorithms for converting pressure readings to air data have become predominant- the iterative Least Squares State Estimator (LSSE) and the Triples Algorithm. What follows herein is a new algorithm that takes advantage of the best features of both the Triples Algorithm and the LSSE. This approach employs the potential flow model and strategic differencing of the Triples Algorithm to obtain the defective flight angles; however, the requirements on port placement are far less restrictive, allowing for configurations that are considered optimal for a FADS.

Millman, Daniel R.↗

NASA NTRS2018

An Intelligent Propulsion Control Architecture to Enable More Autonomous Vehicle Operation

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

autonomy↗

NASA NTRS2017

An Intelligent Propulsion Control Architecture to Enable More Autonomous Vehicle Operation

autonomy↗

NASA NTRSDate not supplied

Second-generation Micro-Spec: Spectrometer Design for the Experiment for Cryogenic Large-Aperture Intensity Mapping

Micro-Spec (μ-Spec) is a direct-detection spectrometer that integrates all the components of a diffraction-grating spectrometer onto a ∼10-cm2 chip using superconducting microstrip transmission lines on a single-crystal silicon substrate. The second-generation μ-Spec has been designed to operate with a spectral resolution of ∼512 in the far-infrared and submillimeter (420–540 GHz, 714–555 μm) wavelength range, a band of interest for NASA’s EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM). EXCLAIM will be a high-altitude balloon mission that is being designed to map the emission of redshifted carbon monoxide and singly-ionized carbon lines over a redshift range 0 < z < 3.5. EXCLAIM will be the first demonstration of the μ-Spec technology in a space-like environment. This work reviews the status of the μ-Spec design for the EXCLAIM instrument, with emphasis on the spectrometer’s two-dimensional diffractive region, through which light of different wavelengths is focused on microwave kinetic inductance detectors (MKIDs) along the spectrometer focal plane. An optimization process generates a geometrical configuration of the diffractive region that satisfies specific requirements on size, operating spectral range and performance. An initial optical design optimized for EXCLAIM is presented in terms of geometric layout, spectral purity and efficiency.

Giuseppe Cataldo↗

NASA NTRS2023

Reduction in Computational Cost of Progressive Failure Analysis of Composite Structures

Designing aircraft structures requires efficient modeling approaches to iterate on multiple structural configurations to achieve an optimal design. Typically, damage tolerance is not considered at the design stage because of the high computational cost in its implementation within a finite element modeling approach. Therefore, analytical or empirical approaches are often used to size critical damage-tolerant structures once an optimal design is determined. In this study, the Progressive Release eXplicit Virtual Crack-Closure Technique (PRX-VCCT) is assessed for its capability to cost-effectively evaluate skin stringer separation of a blade-stiffened panel that is subjected to seven point bend loads. An initial verification study was performed to evaluate PRX VCCT to accurately simulate skin-stringer separation with respect to existing cohesive element approaches. Furthermore, the influence of element size, ranging from 0.10 in. to 0.40 in., on the total computational time using the PRX-VCCT is investigated. The results indicate that the PRX-VCCT can be used to accurately simulate skin stringer separation using large element lengths (0.40 in.). Additionally, a significant reduction in the computational time to simulate skin stringer separation is observed using the PRX-VCCT. Large-scale progressive damage analysis using PRX-VCCT can be implemented early in the design cycle of composite structures without requiring a global-to-local modeling approach.

Composites↗

DOE OSTI2022

Sensitivity Coefficients Calculated for the Prompt Neutron Decay Constant At or Near Delayed Critical

The derivation of a non-invasive prompt neutron decay constant sensitivity coefficient is provided in this work. The computation of the sensitivity coefficient derived in this work does not require modification of Monte Carlo source code and is based on capabilities available in Monte Carlo N-Particle R© Code Version 6.2. The prompt neutron decay constant sensitivity coefficients are calculated for 44-group and 252-group energy structures for specific nuclide-reaction pairs in the Jezebel benchmark experiment. The nuclide-reaction pairs investigated in this work include Pu- 239(n,f), Pu-240(n,f), and Pu-241(n,f). Physical explanations of the sensitivity profiles exhibited by the 252-group energy structure are investigated for the prompt neutron multiplication factor, mean neutron lifetime, and prompt neutron decay constant. The prompt neutron decay constant sensitivity coefficients calculated for the 44-group and 252-group energy structure of Pu-239(n,f) are compared. Lastly, the 44-group energy structure sensitivity coefficients calculated are used for nuclear-data induced uncertainty quantification of the neutron multiplication factor. This work shows that a reduction in the nuclear data-induced uncertainty of the neutron multiplication factor is possible for all nuclide-reaction pairs investigated when prompt neutron decay constant sensitivity coefficients are utilized. This is important for new critical experiment design optimization studies of measurement configurations. This work provides a basis for more detailed sensitivity analysis and uncertainty quantification of nuclide, reaction, and energy-specific cross section data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

The ICEBERG Test Stand for DUNE Cold Electronics Development

ICEBERG is a liquid argon time projection chamber at Fermilab for the purpose of testing detector components and software for the Deep Underground Neutrino Experiment (DUNE). The detector features a 1.15m x 1m anode plane following the specifications of the DUNE horizontal drift far detector and a newly installed X-ARAPUCA photodetector. The status of ICEBERG is reported along with analysis of noise, pulser, and cosmic ray data from the ninth run beginning May 2024 with the goal of advising the DUNE collaboration on the optimal wire readout electronics configuration. In addition, development of an absolute energy scale calibration method is currently underway using known sources such as cosmic ray muon Michel electrons at the ~10 MeV scale and $^{39}$Ar decay electrons at the ~100keV scale. Research into AI-based identification of such events at the data acquisition level is introduced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗

DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

DOE OSTI2020

Computational model for predicting the performance of fluid-powered longiudinal piston modules and motors.

A Matlab-based computational model line code has been developed for predicting the performance of fluid-powered longitudinal piston modules and motors. A system of equations has been developed based upon applying the conservation of mass to a control volume defined by a reciprocating piston. The method results in a differential equation that predicts the pressure gradient in the control volume as a function of rotor rotation angle. The model uses an integration scheme to predict the pressure state in the control volume. The orifice equation is used to predict the flowrate in and out of the control volume. From the resulting solution the torque and speed characteristics of the motor configuration can be predicted, optimized, or tailored for a specific requirement.SAND2019-13983 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Taylor, Michael↗

DOE OSTI2022

Computer-aided process intensification of natural gas to methanol process

The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional methane-to-methanol process is energy intensive which negatively affects the profitability and sustainability. Here an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block-based representation and optimization methodology. The new process configuration utilizes membrane-assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO 2 -equivalent greenhouse gas emission. Such drastic improvement highlights the advantages of building block-based computer-aided process intensification method.

42 ENGINEERING↗

NASA NTRS2012

Recursive Branching Simulated Annealing Algorithm

This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.

Bolcar, Matthew↗

NASA NTRS2023

Passive Rocket Diffuser Testing: Reacting Flow Performance of Advanced Configurations

Upper stage rocket engines are optimized for operation at the edge of space. Ground testing often requires simulation of high-altitude environments to prevent damage from off-design operation. Passive supersonic diffusers are an effective means of establishing the partial vacuum needed around the nozzle exit. The low cost of diffusers relative to active vacuum sources (ejectors, pumps, etc.) provides a perpetual incentive to improve their performance and expand the envelope of passively testable engines. Second-throat diffusers have been the de facto standard for rocket testing since the 1950s, and numerous studies have focused on parametric optimization of the topology. Yet, even the best second-throat designs may require more driving pressure than an upper stage engine can provide. To overcome conventional limits, engineers at NASA’s Stennis Space Center (SSC) adapted an obscure cold-flow diffuser topology to the extremes of immersion in supersonic combustion products. Aerodynamic experimentation was conducted at SSC’s E-3 test stand to quantify the performance of prototypical hot-fire spike diffuser hardware and enable direct comparison to prior states of the art. A LO X /GH 2 thruster was contoured to approximate the regeneratively-cooled nozzle of an upper-stage engine and fired into 28 downstream diffuser configurations: 16 second-throat, 2 centerbody, and 10 spike. Temperature and pressure were recorded along the test article walls for a range of chamber pressures spanning 3.5 - 5.0 MPa. The second-throat configurations with the lowest start and unstart pressure ratios were shown to outperform equivalent systems in literature and taken as baselines for evaluation of advanced diffuser performance. Compared to these exemplars, centerbody diffusers decreased start pressure ratio by 11% but increased unstart pressure ratio by 15%. Spike diffusers reduced start and unstart pressure ratios by 24% and 9%, respectively.

Rocket↗

DOE OSTI2024

Dispatch Optimization Variable Engine

The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations. DOVE is an integral part of the Framework for Optimization of Resources and Economics (FORCE) software suite and is leveraged by codes such as the Holistic Energy Resource Optimization Network (HERON) and the Optimization of Real-Time Capacity Allocation (ORCA). The philosophy behind DOVE is to provide a modular software solution to IES planning and operation by utilizing state-of-the-art algorithms and machine learning. The goal is to accurately capture the dispatching behavior of a complex energy system given varying time-dependent signals for demand and commodity pricing.

McDowell, DylanJ. [Idaho National Laboratory (INL)↗

DOE OSTI2025

Performance Optimization of the IOTA Duoplasmatron Proton Source

We present results from online optimization studies of a duoplasmatron ion source designed to produce 50~keV protons for acceleration to 2.5~MeV and subsequent injection into the Integrable Optics Test Accelerator (IOTA) at Fermilab. Using a Bayesian exploration technique, we developed multi-parameter models of the source s proton current and employed these models to optimize its performance. Depending on the spectrometer configuration used to isolate the proton beam and the chosen optimization objective, we identified three candidate operating points, achieving normalized 50~\% emittances between 0.57 and 1.3~\textmu m and a maximum proton current of $14.5 \pm 0.6$~mA.

Banerjee, Nilanjan [Fermilab] (ORCID:0000000344660↗

DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

NASA NTRS2008

Airbag Landing Impact Performance Optimization for the Orion Crew Module

This report will discuss the use of advanced simulation techniques to optimize the performance of the proposed Orion Crew Module airbag landing system design. The Boeing Company and the National Aeronautic and Space Administration s Langley Research Center collaborated in the analysis of the proposed airbag landing system for the next generation space shuttle replacement, the Orion spacecraft. Using LS-DYNA to simulate the Crew Module landing impacts, two main objectives were established and achieved: the investigation of potential methods of optimizing the airbag performance in order to reduce rebound on the anti-bottoming bags, lower overall landing loads, and increase overall Crew Module stability; and the determination of the Crew Module stability and load boundaries using the optimized airbag design, based on the potential Crew Module landing pitch angles and ground slopes in both the center of gravity forward and aft configurations. This paper describes the optimization and stability and load boundary studies and presents a summary of the results obtained and key lessons learned from this analysis.

Lee, Timothy J.↗