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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Controlling selectivity of modular microbial biosynthesis of butyryl-CoA-derived designer esters

Short-chain esters have broad utility as flavors, fragrances, solvents, and biofuels. Controlling selectivity of ester microbial biosynthesis has been an outstanding metabolic engineering problem. In this study, we enabled the de novo fermentative microbial biosynthesis of butyryl-CoA-derived designer esters (e.g., butyl acetate, ethyl butyrate, butyl butyrate) in Escherichia coli with controllable selectivity. Using the modular design principles, we generated the butyryl-CoA-derived ester pathways as exchangeable production modules compatible with an engineered chassis cell for anaerobic production of designer esters. We designed these modules derived from an acyl-CoA submodule (e.g., acetyl-CoA, butyryl-CoA), an alcohol submodule (e.g., ethanol, butanol), a cofactor regeneration submodule (e.g., NADH), and an alcohol acetyltransferase (AAT) submodule (e.g., ATF1, SAAT) for rapid module construction and optimization by manipulating replication (e.g., plasmid copy number), transcription (e.g., promoters), translation (e.g., codon optimization), pathway enzymes, and pathway induction conditions. To further enhance production of designer esters with high selectivity, we systematically screened various strategies of protein solubilization using protein fusion tags and chaperones to improve the soluble expression of multiple pathway enzymes. Finally, our engineered ester-producing strains could achieve 19-fold increase in butyl acetate production (0.64 g/L, 96% selectivity), 6-fold increase in ethyl butyrate production (0.41 g/L, 86% selectivity), and 13-fold increase in butyl butyrate production (0.45 g/L, 54% selectivity) as compared to the initial strains. Altogether, this study presented a generalizable framework to engineer modular microbial platforms for anaerobic production of butyryl-CoA-derived designer esters from renewable feedstocks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deuterium and tritium anomalous transport in the generalized Hasegawa–Wakatani resistive drift wave turbulence model with finite ion Larmor radius

Anomalous transport of mixed deuterium–tritium plasma in the edge of magnetic fusion reactors is investigated using numerical solutions of resistive drift wave turbulence model equations, including finite Larmor radius effects, that are derived within the generalized Hasegawa–Wakatani framework. The anomalous cross field diffusivities of deuterium and tritium are compared in turbulence regimes with different values of the electron adiabaticity parameter controlling the existence of zonal flow. The dependence of the tritium-to-deuterium diffusivity ratio on the deuterium and tritium densities and the logarithmic density gradients is analyzed, and a scaling relation is obtained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Smart building HVAC control challenge: experience and solutions from the ADRENALIN project

A smart building HVAC control competition crowdsourced and compared algorithms on fair and equal ground using the standardized BOPTEST framework. The competition attracted 138 participants, but only 9% submitted valid solutions for the final stage, highlighting the complexity of advanced HVAC control design. The winning solutions showed significant potential to reduce energy use and cost by shifting demand, without compromising occupant comfort. Across scenarios, thermal energy cost reductions of 36–76% relative to a baseline, were achieved. In peak heat periods, the cost reduction leveraged limited energy use reduction (0–15%), but more significant energy price reduction (34–62%). This shows smart controls' ability to avoid as much as possible consumption during the morning peak hours, when spot prices are tendentially the highest. Hosting the competition has highlighted challenges in creating competitions that both are fair and promotes solutions that are transferable to real life implementation.

BOPTEST↗
DOE OSTI2020

Characterization of turbulence under different stability conditions using lidar scanning data

The spatiotemporal scales of turbulence in the boundary layer flow vary with the static stability of the atmosphere. For example, turbulence structures in unstable conditions are often found to be large and coherent, whereas the turbulent structures in stable conditions are relatively small and sporadic. To better understand the behavior of such turbulent structures under different atmospheric stability conditions in the lower atmosphere, herein, we use scanning lidar data collected over several months during the US Department of Energy's second Wind Forecast Improvement Project (WFIP 2) in 2016. The radial velocity data from scanning lidar were gathered along the horizontal plane (PPI mode) and were analyzed using proper orthogonal decomposition (POD) and spectral energy methods. The results from these analyses show that the turbulent structures in daytime unstable conditions are different from those present during nighttime stable conditions in terms of amount of energy and coherent structures. The POD energy is distributed more evenly in all the higher spatial modes in the daytime unstable case compared to the nighttime stable case. Besides characterizing the turbulence in the flow, the lidar data were also used to compare the simulated flow field generated from the WRF-LES framework (Weather Research and Forecasting - large-eddy simulation) with the scanning lidar data. The results from both simulated and lidar scanned data show similar structures orienting the streak-like structure along the mean wind. This indicates that the streak-like structures can exist in the real atmosphere during unstable atmospheric condition cases.

Rai, Raj K.↗
DOE OSTI2025

alaric parton shower for hadron colliders

We introduce the parton shower for simulating quantum chromodynamics (QCD) radiation at hadron colliders and present numerical results from an implementation in the event generator . provides a consistent framework to quantify certain systematic uncertainties which cannot be eliminated by comparing the parton shower with analytic resummation. In particular, it allows us to study recoil effects away from the soft and collinear limits without the need to change the evolution variable or the splitting functions. We assess the performance of in Drell-Yan lepton pair and QCD jet production, and present the first multijet merging for the new algorithm. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2023

Understanding the transient large amplitude oscillatory shear behavior of yield stress fluids

A full understanding of the sequence of processes exhibited by yield stress fluids under large amplitude oscillatory shearing is developed using multiple experimental and analytical approaches. A novel component rate Lissajous curve, where the rates at which strain is acquired unrecoverably and recoverably are plotted against each other, is introduced and its utility is demonstrated by application to the analytical responses of four simple viscoelastic models. Using the component rate space, yielding and unyielding are identified by changes in the way strain is acquired, from recoverably to unrecoverably and back again. The behaviors are investigated by comparing the experimental results with predictions from the elastic Bingham model that is constructed using the Oldroyd–Prager formalism and the recently proposed continuous model by Kamani, Donley, and Rogers in which yielding is enhanced by rapid acquisition of elastic strain. The physical interpretation gained from the transient large amplitude oscillatory shear (LAOS) data is compared to the results from the analytical sequence of physical processes framework and a novel time-resolved Pipkin space. The component rate figures, therefore, provide an independent test of the interpretations of the sequence of physical processes analysis that can also be applied to other LAOS analysis frameworks. Each of these methods, the component rates, the sequence of physical processes analysis, and the time-resolved Pipkin diagrams, unambigiously identifies the same material physics, showing that yield stress fluids go through a sequence of physical processes that includes elastic deformation, gradual yielding, plastic flow, and gradual unyielding.

Kamani, Krutarth M. (ORCID:0000000338975420)↗
DOE OSTI2022

High Frequency Transverse Instabilities in Low NOx Gas Turbines

This program addresses the topic of combustion instabilities under the goal of developing “Low-NOx Combustion Technology for ‘Air-Breathing’ Advanced Turbines”. With recent advances in high-temperature withstanding materials, the push towards higher efficiency has resulted in efforts to design high temperature, low NOx gas turbines. The development of such low NOx systems is often plagued with combustion instabilities. These instabilities manifest with acoustic modes that are longitudinal, transverse or both. A large body of literature and work exists addressing the underlying mechanisms and models for longitudinal instabilities and these have been successfully implemented in the design tools for low NOx gas turbines. The flame response aspect of the thermoacoustic feedback loop was often modeled in an acoustically compact framework since for low frequencies, the flame length scale was small compared to the acoustic wavelength. This enabled simplification where the modeling focused only on the overall flame response and not the local spatial distribution of the flame response. In addition, additional mechanisms such as the direct effect of pressure fluctuations on the flame response could be neglected. In contrast, high frequency instabilities which often are transverse in nature, have received relatively less attention in the literature. The flame is no longer acoustically compact and thus the local distribution of heat release must be accurately understood. The flame responds to velocity fluctuations, equivalence ratio fluctuations as before, but also to pressure fluctuations through kinetic effects. These aspects of high-frequency instabilities make it a challenging problem that requires a detailed elucidation of the underlying mechanisms and creating models for them. In this program we significantly improved the understanding of these instabilities through a combination of experiments (informed by interactions with OEMs), modeling of measured data and reduced order modeling of flame response. The results from the program can be used as basis for robust design tools that are essential for successfully developing gas turbines that operate in an environmentally acceptable manner with mitigation strategies for high-frequency instabilities.

08 HYDROGEN↗
DOE OSTI2026

Towards Scaling Law Analysis For Spatiotemporal Weather Data

Compute-optimal scaling laws are relatively well studied for NLP and CV, where objectives are typically single-step and targets are comparatively homogeneous. Weather forecasting is harder to characterize in the same framework: autoregressive rollouts compound errors over long horizons, outputs couple many physical channels with disparate scales and predictability, and globally pooled test metrics can disagree sharply with per-channel, late-lead behavior implied by short-horizon training. We extend neural scaling analysis for autoregressive weather forecasting from single-step training loss to long rollouts and per-channel metrics. We quantify (1) how prediction error is distributed across channels and how its growth rate evolves with forecast horizon, (2) if power law scaling holds for test error, relative to rollout length when error is pooled globally, and (3) how that fit varies jointly with horizon and channel for parameter, data, and compute-based scaling axes. We find strong cross-channel and cross-horizon heterogeneity: pooled scaling can look favorable while many channels degrade at late leads. We discuss implications for weighted objectives, horizon-aware curricula, and resource allocation across outputs.

Kiefer Jr, Alexander [ORNL] (ORCID:000000025398874↗
DOE OSTI2023

Sustainable Water Management: Understanding the Socioeconomic and Cultural Dimensions

With the pressing challenges of water scarcity and pollution, achieving sustainable water management is imperative for promoting long-term development. Therefore, this paper aims to examine the socio-economic and cultural factors that shape the sustainability of water management strategies in Brazil and Portugal. This study highlights various factors that influence water management, including robust legal frameworks, socio-economic disparities, cultural practices, agricultural water usage, knowledge sharing, public participation, climate change resilience, water scarcity risks, industrial water consumption, and urbanization. By conducting a SWOT analysis of water management strategies, this research synthesizes information through an extensive literature review, encompassing the legal frameworks, policies, and implemented strategies in both countries. Additionally, it investigates comparative studies among Brazil, Portugal, and other European nations to facilitate the exchange of knowledge and experiences in water management practices. The findings of this study offer valuable insights into the strengths, weaknesses, opportunities, and threats associated with water management strategies in Brazil and Portugal, thereby guiding the development of tailored policies and strategies that foster sustainability in water resource management. Additionally, the research highlights the role of digital transformation in optimizing water management practices. By integrating socio-economic, cultural, and digital factors, this study contributes to effective and sustainable water management in Brazil and Portugal, ensuring responsible utilization and preservation of water resources.

Santos, Eleonora↗
NASA NTRS1983

Structural optimization by multilevel decomposition

A method is described for decomposing an optimization problem into a set of subproblems and a coordination problem which preserves coupling between the subproblems. The method is introduced as a special case of multilevel, multidisciplinary system optimization and its algorithm is fully described for two level optimization for structures assembled of finite elements of arbitrary type. Numerical results are given for an example of a framework to show that the decomposition method converges and yields results comparable to those obtained without decomposition. It is pointed out that optimization by decomposition should reduce the design time by allowing groups of engineers, using different computers to work concurrently on the same large problem.

Sobieszczanski-Sobieski, J.↗
NASA NTRS2001

Biogeochemical Response to Mesoscale Physical Forcing in the California Current System

In the first part of the project, we investigated the local response of the coastal ocean ecosystems (changes in chlorophyll, concentration and chlorophyll, fluorescence quantum yield) to physical forcing by developing and deploying Autonomous Drifting Ocean Stations (ADOS) within several mesoscale features along the U.S. west coast. Also, we compared the temporal and spatial variability registered by sensors mounted in the drifters to that registered by the sensors mounted in the satellites in order to assess the scales of variability that are not resolved by the ocean color satellite. The second part of the project used the existing WOCE SVP Surface Lagrangian drifters to track individual water parcels through time. The individual drifter tracks were used to generate multivariate time series by interpolating/extracting the biological and physical data fields retrieved by remote sensors (ocean color, SST, wind speed and direction, wind stress curl, and sea level topography). The individual time series of the physical data (AVHRR, TOPEX, NCEP) were analyzed against the ocean color (SeaWiFS) time-series to determine the time scale of biological response to the physical forcing. The results from this part of the research is being used to compare the decorrelation scales of chlorophyll from a Lagrangian and Eulerian framework. The results from both parts of this research augmented the necessary time series data needed to investigate the interactions between the ocean mesoscale features, wind, and the biogeochemical processes. Using the historical Lagrangian data sets, we have completed a comparison of the decorrelation scales in both the Eulerian and Lagrangian reference frame for the SeaWiFS data set. We are continuing to investigate how these results might be used in objective mapping efforts.

Niiler, Pearn P.↗
NASA NTRS2006

A Test of Sensitivity to Convective Transport in a Global Atmospheric CO2 Simulation

Two approximations to convective transport have been implemented in an offline chemistry transport model (CTM) to explore the impact on calculated atmospheric CO2 distributions. GlobalCO2 in the year 2000 is simulated using theCTM driven by assimilated meteorological fields from the NASA s Goddard Earth Observation System Data Assimilation System, Version 4 (GEOS-4). The model simulates atmospheric CO2 by adopting the same CO2 emission inventory and dynamical modules as described in Kawa et al. (convective transport scheme denoted as Conv1). Conv1 approximates the convective transport by using the bulk convective mass fluxes to redistribute trace gases. The alternate approximation, Conv2, partitions fluxes into updraft and downdraft, as well as into entrainment and detrainment, and has potential to yield a more realistic simulation of vertical redistribution through deep convection. Replacing Conv1 by Conv2 results in an overestimate of CO2 over biospheric sink regions. The largest discrepancies result in a CO2 difference of about 7.8 ppm in the July NH boreal forest, which is about 30% of the CO2 seasonality for that area. These differences are compared to those produced by emission scenario variations constrained by the framework of Intergovernmental Panel on Climate Change (IPCC) to account for possible land use change and residual terrestrial CO2 sink. It is shown that the overestimated CO2 driven by Conv2 can be offset by introducing these supplemental emissions.

Bian, H.↗
NASA NTRS2021

Changes in TOA SW Fluxes over Marine Clouds When Estimated via Semiphysical Angular Distribution Models

Top-of-atmosphere (TOA) shortwave (SW) angular distribution models (ADMs) approximate—per angular direction of an imagined upward hemisphere—the intensity of sunlight scattered back from a specific Earth–atmosphere scene. ADMs are, thus, critical when converting satellite-borne broadband radiometry into estimated radiative fluxes. This paper applies a set of newly developed ADMs with a more refined scene definition and demonstrates tenable changes in estimated fluxes compared to currently operational ADMs. Newly developed ADMs use a semiphysical framework to consider cloud-top effective radius (¯¯R_e) and above-cloud water vapor (ACWV), in addition to accounting for surface wind speed and clouds’ phase, fraction, and optical depth. In effect, instantaneous TOA SW fluxes for marine liquid-phase clouds had the largest flux differences (of up to 25 W m−2) for lower solar zenith angles and cloud optical depth greater than 10 due to extremes in ¯¯R_e or ACWV. In regions where clouds had persistently extreme levels of ¯¯R_e (here mostly for ¯¯R_e < 7μm and ¯¯R_e > 15μm) or ACWV, instantaneous fluxes estimated from Aqua, Terra, Meteosat-8, and Meteosat-9 satellites using the two ADMs differed systematically, resulting in significant deviations in daily mean fluxes (up to ±10 W m^(−2)) and monthly mean fluxes (up to ±5 W m^(−2)). Flux estimates using newly developed, semiphysical ADMs may contribute to a better understanding of solar fluxes over low-level clouds. It remains to be seen whether aerosol indirect effects are impacted by these updates.

angular distribution models (ADMs)↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
DOE OSTI2025

A Bayesian framework to investigate radiation reaction in strong fields

Recent experiments aiming to measure phenomena predicted by strong-field quantum electrodynamics (SFQED) have done so by colliding relativistic electron beams and high-power lasers. In such experiments, measurements of collision parameters are not always feasible. However, precise knowledge of these parameters is required to accurately test SFQED. Here, we present a novel Bayesian inference procedure that infers collision parameters that could not be measured on-shot. This procedure is applicable to all-optical non-linear Compton scattering experiments investigating radiation reaction. The framework allows multiple diagnostics to be combined self-consistently and facilitates the inclusion of known information pertaining to the collision parameters. Using this Bayesian analysis, the relative validity of the classical, quantum-continuous and quantum-stochastic models of radiation reaction was compared for several test cases, which demonstrates the accuracy and model selection capability of the framework and highlight its robustness if the experimental values of fixed parameters differ from their values in the models.

47 OTHER INSTRUMENTATION↗
NASA NTRS2007

GVIPS Models and Software

Two reports discuss, respectively, (1) the generalized viscoplasticity with potential structure (GVIPS) class of mathematical models and (2) the Constitutive Material Parameter Estimator (COMPARE) computer program. GVIPS models are constructed within a thermodynamics- and potential-based theoretical framework, wherein one uses internal state variables and derives constitutive equations for both the reversible (elastic) and the irreversible (viscoplastic) behaviors of materials. Because of the underlying potential structure, GVIPS models not only capture a variety of material behaviors but also are very computationally efficient. COMPARE comprises (1) an analysis core and (2) a C++-language subprogram that implements a Windows-based graphical user interface (GUI) for controlling the core. The GUI relieves the user of the sometimes tedious task of preparing data for the analysis core, freeing the user to concentrate on the task of fitting experimental data and ultimately obtaining a set of material parameters. The analysis core consists of three modules: one for GVIPS material models, an analysis module containing a specialized finite-element solution algorithm, and an optimization module. COMPARE solves the problem of finding GVIPS material parameters in the manner of a design-optimization problem in which the parameters are the design variables.

Arnold, Steven M.↗