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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 343 records · Page 19
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NASA NTRS2019

LUVOIR: Engineering Design & Technology Overview

The Large Ultraviolet / Optical / Infrared Surveyor (LUVOIR) is one of four large mission concepts being study by NASA in preparation for the 2020 Decadal Survey in Astronomy and Astrophysics. Over the past three and a half years, the LUVOIR Science and Technology Definition Team (STDT) and Study Office at NASA's Goddard Space Flight Center have developed a broad, compelling science case for LUVOIR, and detailed engineering point designs to achieve it. In this paper, we provide an overview of the two LUVOIR concepts: LUVOIR-A, a 15-m segmented, obscured aperture observatory, and LUVOIR-B, an 8-m segmented, unobscured aperture observatory. Both versions of LUVOIR cover a broad spectral range between 100 nm and 2.5 µm with a suite of imagers and spectrographs, including the High Definition Imager (HDI), the LUVOIR Ultraviolet Multi-object Spectrograph (LUMOS), and the Extreme Coronagraph for Living Planetary Systems (ECLIPS). LUVOIR-A will carry an additional fourth instrument, Pollux, a high-resolution UV spectropolarimeter being studied by the Centre National d'Etudes Spatiales (CNES). Both versions of LUVOIR are also designed to be serviceable and upgradeable. We will also provide a summary of the LUVOIR technology development program, which identifies critical enabling technologies, enhancing technologies, and a development plan to mature those technologies to TRL 6.

technology↗
NASA NTRS2019

Overview of the Optomechanical Design of the LUVOIR Instruments

The Large Ultraviolet/Optical/Infrared Surveyor (LUVOIR) is a space telescope being submitted for review to the 2020 Decadal Survey in Astronomy and Astrophysics. Its science objectives include both direct imaging and spectral characterization of habitable exoplanets around sun-like stars, the study of planet, star, and galaxy formation, the transfer of matter between different galaxies, and the remote sensing of objects within the Solar System. Two architectures have been designed: a 15 m diameter on-axis telescope (LUVOIR-A) and an 8 m off-axis telescope (LUVOIR-B). This paper discusses the opto-mechanical design of the three LUVOIR instruments: the High Definition Imager (HDI), the LUVOIR UV Multi-object Spectrograph (LUMOS), and the Extreme Coronagraph for Living Planetary Systems (ECLIPS). For both the LUVOIR-A and LUVOIR-B variants of each instrument, optical design specifications are presented including first-order constraints, packaging requirements, and optical performance metrics. These factors are used to illustrate the final design of each instrument and LUVOIR as a whole. In addition to the optical designs, mechanical models are presented for each instrument showing the optical mounts, mechanisms, support structure, etc.

Decadal Study↗
NASA NTRS2019

Conceptual Design of a Counter-Rotating Fan System for Distributed Boundary Layer Ingesting Propulsion

The present paper details the design of the counter rotating fans for a Turboelectric Distributed Propulsion (TeDP) system. Sixteen propulsors installed in mail-slot-shape nacelles are embedded on an aerodynamically optimized hybrid wing-body configuration. The hybrid-wing/body (HWB) configuration which was previously designed to satisfy the conditions of trim, longitudinally static stability and specific cargo space is employed as the baseline configuration in pursuing an optimal distributed propulsion system. A set of distributed propulsors is conceptually designed and the collective performance is evaluated against the target thrust mandated by the mission requirements. The concept of the distributed propulsion allows the fan pressure ratio to be around 1.27~1.32 for the target thrust. In addition, further splitting of the fan pressure ratio by using the counter-rotating fans for each slot realizes the target pressure ratio with low tip speed. In the distributed propulsion system, the nature of the flow conditions and/or the thickness of the ingested boundary layer may differ and result in different propulsive reaction of each individual propulsor. The optimization is, thus, approached from both the propulsion system and individual propulsor perspectives. An optimal distribution of the thrust and power output is determined by how the system utilizes each passage's propulsive characteristics and its interaction with the airframe. These system level analysis and optimization are conducted using an actuator disk model to account for the propulsion-airframe integration numerically. With respect to the propulsor level, aerodynamic shape optimizations of the fan blades are performed in a sequential multi-objective optimization process for various design objectives, such as mass flow rate condition, fan pressure ratio, efficiency and the exit flow angle of the fan stage by using a genetic algorithm, NSGA-II. The radial chord distribution, and meanline distribution of the rotors are designed on the circumferentially averaged axi-symmetric inlet profiles and tested on the six inlet profiles from six divided sectors to reckon flow distortion. The performances of the counter rotating fans are, thus, evaluated accordingly for obtaining distortion tolerant fan. The performance of the distributed propulsion system is evaluated by two CFD tools, i.e., a multi-stage turbo-machinery CFD code and one propulsion-airframe integration flow solver coupled with a body-force model. The optimized boundary layer ingestion propulsion system of 16 distributed slots not only reaches the system target thrust, but also delivers a close to 20% fuel saving benefit against its counterpart 12 distributed clean inlet propulsion system.

Boundary-Layer-Ingestion Propulsion↗
NASA NTRS2019

Actuation Force Study of Next Generation Micro Shutter Array for More Reliable Actuation

Micro shutter array (MSA) is a programmable optical spatial filtering device fabricated by MEMS technology, which was originally developed for James Webb Space Telescope to enable multi-object spectroscopy. Next Generation Micro Shutter Array (NGMSA) is an effort to improve JWST MSA by eliminating macroscopic actuation magnet and using electrostatic force to achieve simple and reliable instrument that can support NASA’s multiple future missions. We have been fabricating NGMSA with design inherited from JWST, however the legacy design is not friendly for electrostatic actuation because applied force direction on the moving shutter blade is different. We use COMSOL AC/DC module’s electrostatics feature to understand electrostatic force applied on an individual shutter within a range of motion and study new candidate designs. Also, structural mechanics module is used to study torsion bar’s restoration force.

Kim, K.↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRS2019

The James Webb Space Telescope: Mission Overview and Status

The James Webb Space Telescope (JWST) is the scientific successor to the Hubble Space Telescope. It is a cryogenic infrared space observatory with a 25 m2 aperture (6 m class) telescope that will achieve diffraction limited angular resolution at a wavelength of 2 um. The science instrument payload includes four passively cooled near-infrared instruments providing broad- and narrow-band imagery, coronography, as well as multi-object and integral-field spectroscopy over the 0.6 < λ < 5.0 µm spectrum. An actively cooled mid-infrared instrument provides broad-band imagery, coronography, and integral-field spectroscopy over the 5.0 < λ < 29 µm spectrum. The JWST is being developed by NASA, in partnership with the European and Canadian Space Agencies, as a general user facility with science observations proposed by the international astronomical community in a manner similar to the Hubble Space Telescope. Technology development and mission design are complete. Construction, integration and verification testing is underway in all areas of the program. The JWST is on schedule for launch during 2021.

Greenhouse, Matthew↗
NASA NTRSDate not supplied

TPSAS-NF1676L-15304-DND

The Subsonic Fixed Wing Project conducts research to improve prediction methods and technologies that will produce lower noise, lower emissions, and higher performing subsonic aircraft for the Next Generation Air Transportation System. Subproject: FW Structural Efficiency: Lightweight, multi-objective and multifunctional structures (e.g., structures that can carry load and attenuate noise at the same time) are being developed to reduce the weight of both airframes and engines. Adaptive and flexible wing structures with aeroelastic tailoring offer significant weight reduction due to the elimination of a heavy actuation system. Extensive use of polymer matrix composites (PMCs) in airframe and engine structures (where applicable) will offer significant weight reduction opportunities. Research on composite technology is focused on developing alternate processes that will enable fabrication of large structures, increasing the temperature capability toward greater usage in engine and supersonic airframe structures, developing advanced structural concepts, and modeling damage initiation and progression in composite structures.

Jason P Moore↗
NASA NTRS2004

Evolutionary computing for spacecraft power subsystem design search and optimization

Multi-objective optimization involves finding one or more optimal solutions when there is more than one conflicting objective. This means that a solution that is better in one objective compromises or trades-off, other objectives. Trade Studies are conducted by flight projects to create mission concepts with different trade-off solutions for mass, cost, performance and risk.

Hua, Hook↗
NASA NTRS2018

HabEx Space Telescope Optical System Overview: General Astrophysics Instruments

The HabEx (Habitable Exoplanet) concept study is defining a future space telescope with the primary mission of detecting and characterizing planetary systems around nearby stars. The telescope baseline design includes a high-contrast coronagraph and a starshade to enable the direct optical detection of exoplanets as close as 70 mas to their star. In addition to the study of exoplanets, HabEx carries two dedicated instruments for general astrophysics. The first instrument is a camera enabling imaging on a 3 arc minute field of view in two bands stretching from the UV at 150 nm to the near infrared at 1800 nm. The same instrument can also be operated as a multi-object spectrograph, with resolution of 2000. The second instrument is a high-resolution UV spectrograph operating from 300 nm down to 115 nm with up to 60,0000 resolution. HabEx would provide the highest resolution UV/optical images ever obtained. Diffraction limited at 0.4 µm, it would outperform all current and approved facilities, including the 30 m class ground-based extremely large telescopes (ELTs), which will achieve ~0.01 arcsecond resolution at near-infrared (IR) wavelengths with adaptive optics, but will be seeing-limited at optical wavelengths. HabEx would observe wavelengths inaccessible from the ground, including the UV and in optical/near-IR atmospheric absorption bands. Operating at L2, far above the Earth’s atmosphere and free from the large thermal swings inherent to HST’s low-Earth orbit, HabEx would provide an ultra-stable platform that will enable science ranging from precision astrometry to the most sensitive weak lensing maps ever obtained. Here we discuss the design concepts of the general astrophysics optical instruments for the proposed observatory.

Nissen, Joel↗
NASA NTRS2020

Peter Pan Disks: Long-lived Accretion Disks Around Young M Stars

WISEA J080822.18–644357.3, an M star in the Carina association, exhibits extreme infrared excess and accretion activity at an age greater than the expected accretion disk lifetime. We consider J0808 as the prototypical example of a class of M star accretion disks at ages ≳20 Myr, which we call "Peter Pan" disks, because they apparently refuse to grow up. We present four new Peter Pan disk candidates identified via the Disk Detective citizen science project, coupled with Gaia astrometry. We find that WISEA J044634.16–262756.1 and WISEA J094900.65–713803.1 both exhibit significant infrared excess after accounting for nearby stars within the Two Micron All Sky Survey (2MASS) beams. The J0446 system has >95% likelihood of Columba membership. The J0949 system shows >95% likelihood of Carina membership. We present new Gemini Multi-Object Spectrograph optical spectra of all four objects, showing possible accretion signatures on all four stars. We present ground-based and TESS light curves of J0808 and 2MASS J0501–4337, including a large flare and aperiodic dipping activity on J0808, and strong periodicity on J0501. We find Paβ and Brγ emission indicating ongoing accretion in near-IR spectroscopy of J0808. Using observed characteristics of these systems, we discuss mechanisms that lead to accretion disks at ages ≳20 Myr, and find that these objects most plausibly represent long-lived CO-poor primordial disks, or "hybrid" disks, exhibiting both debris and primordial-disk features. The question remains: why have gas-rich disks persisted so long around these particular stars?

Steven M. Silverberg↗
NASA NTRS2020

The Habitable Exoplanet Observatory (HabE) Mission Concept

The Habitable Exoplanet Observatory (HabEx), was one of four candidate flagship missions studied in detail by NASA, which were submitted for consideration to the 2020 Decadal Survey in Astronomy and Astrophysics for possible launch in the 2030s. For the first time in human history, technologies have matured sufficiently to enable an affordable spacebased telescope mission capable of discovering and characterizing Earthlike planets orbiting nearby bright sunlike stars to search for signs of habitability and biosignatures. Such a mission can also be equipped with instrumentation that will enable broad and exciting general astrophysics and planetary science not possible from current or planned facilities. HabEx was designed to be the Great Observatory of the 2030s and beyond, with unique imaging and multi-object spectroscopic capabilities at wavelengths ranging from ultraviolet (UV) to near-IR.

Warfield, Keith↗
DOE OSTI2025

A Joint Search for the Electromagnetic Counterpart to the Gravitational-Wave Binary Black-Hole Merger Candidate S250328ae with the Dark Energy Camera and the Prime Focus Spectrograph

The first detection of an optical counterpart to a gravitational wave signal revealed that collaborative efforts between instruments with different specializations provide a unique opportunity to acquire impactful multi-messenger data. We present results of such a joint search with the Dark Energy Camera (DECam) and Prime Focus Spectrograph (PFS) for the optical counterpart of the LIGO-Virgo-KAGRA event S250328ae, a binary black hole merger candidate of high significance detected at a distance of 511$\pm$82 Mpc and localized within an area of 3 (15) square degrees at 50% (90%) confidence. We observed the 90% confidence area with DECam and identified 36 high-confidence transient candidates after image processing, candidate selection, and candidate vetting. We observed with PFS to obtain optical spectra of DECam candidates, Swift-XRT candidates, and potential host galaxies of S250328ae. In total, 3897 targets were observed by seven pointings covering ~50% of the 90% confidence area. After template fitting and visual inspection, we identified 12 SNe, 159 QSOs, 2975 galaxies, and 131 stars. With the joint observations of DECam and PFS, we found variability in 12 SNe, 139 QSOs, 37 galaxies, and 2 stars. We do not identify any confident optical counterparts, though the association is not ruled out for three variable candidates that are not observed by PFS and 6 QSO candidates without clear variability if the optical counterpart of S250328ae is faint. Despite the lack of confident optical counterparts, this paper serves as a framework for future collaborations between wide-field imagers and multi-object spectrographs to maximize multi-messenger analyses.

Zhang, Haibin [Natl. Astron. Observ. of Japan] (OR↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
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

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Implementation of fuel management multi-cycle optimization capabilities in RAVEN optimization framework

Optimization in nuclear fuel-management assists the core reload engineer with finding optimal out-of-core and in-core strategies. RAVEN is INL’s open source software that is equipped with fuel-management optimization capabilities including single-cycle, single- and multi-objective optimization of pressurized water reactors (PWRs) loading patterns (LP) of a fresh core using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II). In practice, however, medium and long term planning of fuel-management needs a multi-cycle approach, where the history and availability of fuel assemblies is considered in the optimization process. In this paper, we present a description of an initial expansion of RAVEN fuel-management optimization capabilities for a multi-cycle optimization framework. N-th cycle optimization capabilities that account for the unique history of recycled fuel assembly in the core were added. The multi-cycle optimization approach taken is formulated as a cycle-wise optimization problem where out-of-core decisions are used to onset each cycle optimization. Out-of-core decisions are managed externally to the in-core optimization by a fuel inventory management module. A proof-of-concept optimization problem is also presented.

42 - ENGINEERING↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗