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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Floating Wind Turbine Control Optimization

We present a framework for optimizing the control parameters of floating offshore wind turbines (FOWTs). The framework combines aeroelastic simulations with a systems engineering model and control software. In an example of the optimization framework, we minimize tower damage equivalent loading with generator speed constraints. We also study the effect of thrust-limiting control and quantify the trade off between fatigue loading and energy capture using a set of optimal controller designs. Finally, we optimize the controller of four different FOWT models and compare their dynamic responses. Additional details and other use cases for the framework are presented, which can optimize different control problems and evaluate FOWT designs.

controls↗
DOE OSTI2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.

Vatsavai, Sairam Sri [Brookhaven National Laborato↗
DOE OSTI2023

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Abstract In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the Large Hadron Collider (LHC), there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE’s limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in 18.30 ± 0.04 μ s , making it one of the fastest methods for this task up to now.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2014

Aerodynamic Limits on Large Civil Tiltrotor Sizing and Efficiency

The NASA Large Civil Tiltrotor (2nd generation, or LCTR2) is a useful reference design for technology impact studies. The present paper takes a broad view of technology assessment by examining the extremes of what aerodynamic improvements might hope to accomplish. Performance was analyzed with aerodynamically idealized rotor, wing, and airframe, representing the physical limits of a large tiltrotor. The analysis was repeated with more realistic assumptions, which revealed that increased maximum rotor lift capability is potentially more effective in improving overall vehicle efficiency than higher rotor or wing efficiency. To balance these purely theoretical studies, some practical limitations on airframe layout are also discussed, along with their implications for wing design. Performance of a less efficient but more practical aircraft with non-tilting nacelles is presented.

Aerodynamics Limits↗
DOE OSTI2025

Thermal Modeling and Limitations for Power Electronics Embedded in Medium-Voltage Cables

As next-generation energy technologies gain traction and power demand increases, the existing electrical infrastructure faces significant stress, prompting innovative solutions to enhance the grid's capacity and lifespan. This work explores the possibility of embedding medium-voltage (MV) power electronics directly inline with the cable, and the resulting thermal challenges. Since the majority of power distribution cables installed in the U.S. are passively cooled, the work focuses primarily on passive cooling, with an emphasis on the limitations of axial heat spreading within the cable. To date, literature on axial spreading of high incident heat loads on cables and cable environments is limited, typically reporting cases with <10 W of incident heat load. This work will explore the considerations, limits, and tradeoffs of cable-embedded heat loads significantly larger than the cable losses. Both external and internal effects are modeled analytically in nondimensional terms via a Biot number analysis, allowing fundamental limits and tradeoffs to be derived. The work culminates in the design and experimental validation of a cable-embedded thermal system capable of passively dissipating 300 W of heat from a coaxial SiC mosfet switch module over a length of 20 cm, thus validating the possibility of MV cable-embedded power electronics from a thermal standpoint.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Data-Driven Model for Photovoltaic Generation: Comparison with Physical Models Using a Microgrid in Puerto Rico

Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.

R pesante colón, Marcos↗
NASA NTRS2001

High Efficiency Microwave Power Amplifier: From the Lab to Industry

Since the beginnings of space travel, various microwave power amplifier designs have been employed. These included Class-A, -B, and -C bias arrangements. However, shared limitation of these topologies is the inherent high total consumption of input power associated with the generation of radio frequency (RF)/microwave power. The power amplifier has always been the largest drain for the limited available power on the spacecraft. Typically, the conversion efficiency of a microwave power amplifier is 10 to 20%. For a typical microwave power amplifier of 20 watts, input DC power of at least 100 watts is required. Such a large demand for input power suggests that a better method of RF/microwave power generation is required. The price paid for using a linear amplifier where high linearity is unnecessary includes higher initial and operating costs, lower DC-to-RF conversion efficiency, high power consumption, higher power dissipation and the accompanying need for higher capacity heat removal means, and an amplifier that is more prone to parasitic oscillation. The first use of a higher efficiency mode of power generation was described by Baxandall in 1959. This higher efficiency mode, Class-D, is achieved through distinct switching techniques to reduce the power losses associated with switching, conduction, and gate drive losses of a given transistor.

Sims, William Herbert, III↗
DOE OSTI2021

Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Small-scale Assessment of Simplified Ceramic Waste Form Processing

The feasibility of directly processing glass-bonded sodalite ceramic waste form materials by heating a mixture of Zeolite 4A, chloride salt, and sodium borosilicate binder glass was demonstrated by generating laboratory-scale materials. This direct processing method eliminates pre-setting the moisture content of the Zeolite 4A, eliminates the step of occluding salt in the prepared Zeolite 4A prior to processing, and can be conducted using larger particle sizes of crushed Zeolite 4A and crushed borosilicate glass than those called for in the current method. These simplifications are expected to facilitate material transfer and handling in a hot cell or controlled atmosphere environment and be more readily implemented at large scale than the current method. Most materials made to assess these simplifications were directly processed at 925 °C for two hours in an argon atmosphere glovebox, but one material was processed at 925 °C for four hours and one material was processed at 880 °C for two hours. Sodalite was generated and became microencapsulated by the binder glass in all materials. The microstructures were uniform throughout each product, were similar in all products, and were similar to the microstructures of materials made previously using the pressureless consolidation or hot isostatic pressing methods. Various formulations showed the efficiency of sodalite generation was not sensitive to the salt-to-Zeolite 4A ratio or salt-to-glass mass ratio, although a greater relative mass of glass is required to encapsulate sodalite generated from large particles of aggregated Zeolite 4A. The upper limit of salt loadings that can be effectively processed remains to be determined. The effectiveness of direct processing provided new insights into the conversion mechanism. The salt was likely dissolved into the glass that transported NaCl into the Zeolite 4A aggregates and sodalite was generated in situ as NaCl migrated from the outside of the aggregate inward. Other salt cations (e.g., potassium, strontium, cesium, and probably lithium) remain dissolved in the glass encapsulating the zeolite/sodalite domains. When the glass solidifies during cooling, small halite inclusion phases form in glass within sodalite domains and large mixed salt inclusions form in glass surrounding the sodalite due to the low solubility of chloride in the (solid) glass. Other salt cations were oxidized during processing and formed inclusions in the bulk glass (e.g., neodymium). Initial degradation tests show the dissolution behavior of directly processed CWF (SCWF) materials is similar to CWF materials made using different methods.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Evaluation of Hardware and Software Bill of Materials (HBOMs/SBOMs) Extraction Methods

Hardware and software bills of materials (HBOMs and SBOMs) provide important visibility into the components, dependencies, and supply chain relationships within programmable digital devices. This visibility is critical for advanced nuclear reactor applications, where use of common or shared hardware components, software libraries, suppliers, or manufacturing processes may create common cause failure (CCF) vulnerabilities despite apparent diversity. This paper evaluates current approaches for obtaining and analyzing HBOMs and SBOMs in support of CCF, diversity and defense-in-depth (D3) assessments, and begins to explore potential methods for artificial intelligence/machine learning-based analysis. The availability of BOM information from advanced reactor manufacturers and vendors, representative hardware and software categories found in advanced reactor systems continues to limit research [13]. This paper compares commonly used BOM formats, including CycloneDX, SPDX, and SWID. It also surveys publicly available tools for generating BOMs from source code, compiled binaries, and hardware-related information, noting limitations in language coverage, system age, and format interoperability. Finally, this paper evaluates methods for correlating BOM data with vulnerability and exploitability information, including VEX, CVE, and CWE resources. The findings indicate that publicly available nuclear-vendor BOMs are limited, making third-party extraction and research into novel analysis techniques necessary.

Cybersecurity↗
NASA NTRS1991

Digital test signal generation: An accurate SNR calibration approach for the DSN

A new method of generating analog test signals with accurate signal to noise ratios (SNRs) is described. High accuracy will be obtained by simultaneous generation of digital noise and signal spectra at a given baseband or bandpass limited bandwidth. The digital synthesis will provide a test signal embedded in noise with the statistical properties of a stationary random process. Accuracy will only be dependent on test integration time with a limit imposed by the system quantization noise (expected to be 0.02 dB). Setability will be approximately 0.1 dB. The first digital SNR generator to provide baseband test signals is being built and will be available in early 1991.

Gutierrez-Luaces, B. O.↗
DOE OSTI2023

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design

Abstract Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure–property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure—an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗
DOE OSTI2021

Enhanced Integer Permutation based Genetic Algorithm for Optimization of Tube-Fin Heat Exchanger Circuitry with Splits and Merges

Tube-fin heat exchangers (HXs) are widely used in air-conditioning and heat pump applications. The performance of these heat exchangers is strongly influenced by the refrigerant circuitry. Studies have proved that by optimizing the refrigerant circuitry, the performance of HXs can be significantly improved. In our previous research, an Integer Permutation based Genetic Algorithm (IPGA) was developed to obtain the optimal circuitry designs. Our previous research showed that IPGA demonstrates superior capability to obtain better refrigerant circuitries with lower computational cost than the other methods in literature. And the optimal circuitry designs obtained from IPGA are manufacturable with the available tooling. However, the IPGA developed previously cannot generate designs with splitting and merging of circuits. To remedy this limitation, a new chromosome which can represent circuitry with splitting and merging of circuits is developed. In addition to the six genetic operators implemented previously, two new genetic operators are developed to generate splits and merges. As a result, the enhanced IPGA can explore the solution space more thoroughly than the previous IPGA. A case study using an evaporator from an A-type indoor unit shows that, given the similar capacity improvements obtained from the enhanced IPGA compared with the previous IPGA, the refrigerant pressure drop reduction obtained from the enhanced IPGA is 26.5% compared against 1.0% pressure drop reduction from the previous IPGA. The benchmark of the enhanced IPGA with other methods in literature demonstrates that the enhanced IPGA can generate circuitry designs with performance superior to those obtained from other methods.

LI, Zhenning↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. The open-access GeneLab repository currently consists of over 300 omics datasets generated by biological experiments, involving various model organisms, that are relevant to spaceflight. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. To aide GeneLab’s efforts to harmonize and democratize space-relevant omics data, over 130 scientists have joined one of four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG) and together helped develop and adopted standard data analysis workflows for all data types available in GeneLab. Currently, the GeneLab Data System includes a data repository with federated search capability, an online controlled-access toolshed powered by "Galaxy" for users to process data with vetted standard workflows, a workspace for data sharing, a data submission portal, and the ability to browse and visualize transcriptomics processed data. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. The visualization portal enhances GeneLab’s ability to democratize omics data by removing the need for bioinformatics expertise to interpret transcriptomics data hosted on GeneLab. This presentation will provide an over-view of NASA’s GeneLab including how to navigate the GeneLab Data System and will conclude by providing resources for opportunities to work with GeneLab and NASA at large.

Amanda M Saravia-Butler↗
DOE OSTI2024

SCIMON: Scientific Inspiration Machines Optimized for Novelty

We explore and enhance the ability of neu- ral language models to generate novel scien- tific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction— severely limiting the expressivity of hypothe- ses. This line of work also does not focus on optimizing novelty. We take a dramatic depar- ture with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natu- ral language ideas grounded in literature. We present SCIMON, a modeling framework that uses retrieval of “inspirations” from past scien- tific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and up- dating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with over- all low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature.

Ji, Heng↗
DOE OSTI2024

Digital twin advanced distribution management systems (ADMS) and methods

Advanced Distribution Management Systems not generally optimize over the entire feeder because there are few high-fidelity distribution circuit models and real-time distribution-connected sensors are rare. The limited observability at the distribution level makes it difficult to globally optimize distribution operations and issue control setpoints to power systems equipment or Distributed Energy Resources (DER) to perform grid-support services. For example, setpoints can be issued to DER based on results from an optimization module that incorporates a static or time-series feeder simulation. Feeder simulation initial conditions are populated with photovoltaic (PV) and load forecasts, state estimation results, and/or digital twin measurements or state output. The real-time (RT) digital twin runs a model of the feeder to generate state estimation pseudo-measurements since there are limited live feeder measurements.

Johnson, Jay Tillay↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗