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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Reusable Launch Vehicle Control In Multiple Time Scale Sliding Modes

A reusable launch vehicle control problem during ascent is addressed via multiple-time scaled continuous sliding mode control. The proposed sliding mode controller utilizes a two-loop structure and provides robust, de-coupled tracking of both orientation angle command profiles and angular rate command profiles in the presence of bounded external disturbances and plant uncertainties. Sliding mode control causes the angular rate and orientation angle tracking error dynamics to be constrained to linear, de-coupled, homogeneous, and vector valued differential equations with desired eigenvalues placement. Overall stability of a two-loop control system is addressed. An optimal control allocation algorithm is designed that allocates torque commands into end-effector deflection commands, which are executed by the actuators. The dual-time scale sliding mode controller was designed for the X-33 technology demonstration sub-orbital launch vehicle in the launch mode. Simulation results show that the designed controller provides robust, accurate, de-coupled tracking of the orientation angle command profiles in presence of external disturbances and vehicle inertia uncertainties. This is a significant advancement in performance over that achieved with linear, gain scheduled control systems currently being used for launch vehicles.

Shtessel, Yuri↗
DOE OSTI2017

Optimisation of the parameters of a broadband multibeam-pumped optical parametric amplifier

We report the results of the numerical simulation of the output amplifier stage of a high-power laser system based on parametric amplification of femtosecond pulses in an LBO crystal under multibeam pumping. A technique is described for choosing an optimal amplifier configuration with an arbitrary number of pump beams to provide a wide spectral gain band with a minimum loss due to parametric diffraction. The results of simulating the parametric amplification, amplification efficiency, and spatial profile of amplified radiation with allowance for the interference of amplified parasitic beams are presented. A possibility of eliminating the spatial modulation of the amplified radiation intensity, which occurs when using spatial filters, is demonstrated. An increase in the number of pump beams is found to rise the amplified pulse duration, slightly (from 20 to 21 fs) for incoherent pump beams and more significantly (to 25 fs) for coherent beams. The amplification efficiency is reduced from 23% to 21% in the former case and to 11% in the latter case. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1991

Microstrip Yagi array antenna for mobile satellite vehicle application

A novel antenna structure formed by combining the Yagi-Uda array concept and the microstrip radiator technique is discussed. This antenna, called the microstrip Yagi array, has been developed for the mobile satellite (MSAT) system as a low-profile, low-cost, and mechanically steered medium-gain land-vehicle antenna. With the antenna's active patches (driven elements) and parasitic patches (reflector and director elements) located on the same horizontal plane, the main beam of the array can be tilted, by the effect of mutual coupling, in the elevation direction providing optimal coverage for users in the continental United States. Because the parasitic patches are not connected to any of the lossy RF power distributing circuit the antenna is an efficient radiating system. With the complete monopulse beamforming and power distributing circuits etched on a single thin stripline board underneath the microstrip Yagi array, the overall L-band antenna system has achieved a very low profile for vehicle's rooftop mounting, as well as a low manufacturing cost. Experimental results demonstrate the performance of this antenna.

Huang, John↗
NASA NTRS2006

New Raman Water Vapor and Temperature Lidar at JPL Table Mountain Facility: Optimization, Validations and Sonde Intercomparison

Jet Propulsion Laboratory currently operates lidar systems at Table Mountain Facility (TMF), California (34.4 deg N, 117.7 deg W at 2300m) and Mauna Loa Observatory (MLO), Hawaii (19.5 deg N, 155.6 deg W at 3400m) under the Network for the Detection of Atmospheric Composition Change (NDACC, formerly NDSC). To complement existing NDACC lidars at TMF, which acts as a primary site for inter-comparisons, a new water vapor and temperature lidar has begun routine operation with typically 3-4 nightly profiles per week. As water vapor is a key greenhouse gas, and is highly variable on annual and seasonal cycles, accurate long term measurements are necessary for predictions of climate change and to increase our understanding of the atmospheric processes it contributes to. The new TMF lidar has demonstrated high spatial and temporal resolution, with a high degree of optimization being achieved over the past year, although the authors believe further improvement may yet be possible. The lidar has been designed for accuracies of 5% up to 12km in the free troposphere with the capability to measure to the tropopause and lower stratosphere with accuracies of 1 ppm. It is anticipated that the data sets produced will be used for Aura validation and for incorporation into NDACC archives. Validation results for the optimized system are presented with intercomparisons using Vaisala RS92-K radiosondes.

water vapor↗
DOE OSTI2023

CFD modeling of crystallization fouling with CO 2 desorption incorporated for a falling-film evaporator in thermal desalination

Seawater flowing around horizontal tubes in falling-film evaporators is a common configuration for thermal desalination. Heat is transferred from in-tube steam condensation to the shell-side seawater by conduction through the tube wall and scaling layer, and conduction and convection to the evaporating liquid film. CO 2 is simultaneously released from seawater and mixed with the produced vapor. Fouling coupled with CO 2 desorption has deleterious impacts on both evaporation and condensation. The evaluation of spatiotemporal dependent crystallization fouling and CO 2 desorption is vital to selecting optimal operating conditions. Here, in this work, a CO 2 desorption model is integrated into CFD modeling for predictions of coupled heat and mass transfer, to understand and predict scale formation and CO 2 desorption with local and transient profiles of temperature, carbonate species concentrations, pH, and total alkalinity. The porosity of the scale layer is experimentally determined as 67.7% and invoked in the calculation of effective thermal conductivity. The simulation results reveal that high steam temperatures increase seawater total alkalinity and accelerate scale formation. The scale thickness on the bottom tube reaches 0.30 μm and 5.24 μm for steam temperatures of 60 °C and 80 °C, respectively. High salinity leads to a large CO 2 desorption rates. The CO 2 desorption rate increases 43% when the seawater salinity increases from 35 to 55 g/kg. The effects of operating conditions on carbonate speciation and pH have been compared and analyzed. This model can serve as a comprehensive tool for the design of thermal desalination systems and optimal operation.

42 ENGINEERING↗
DOE OSTI2017

Identification of quinazoline based inhibitors of IRAK4 for the treatment of inflammation

Interleukin-1 receptor associated kinase 4 (IRAK4) has been implicated in IL-1R and TLR based signaling. Therefore selective inhibition of the kinase activity of this protein represents an attractive target for the treatment of inflammatory diseases. Medicinal chemistry optimization of high throughput screening (HTS) hits with the help of structure based drug design led to the identification of orally-bioavailable quinazoline based IRAK4 inhibitors with excellent pharmacokinetic profile and kinase selectivity. These highly selective IRAK4 compounds show activity in vivo via oral dosing in a TLR7 driven model of inflammation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Experimental considerations and applications for time-resolved impedance on silicon-anode battery systems

Dynamic electrochemical impedance spectroscopy (dEIS) utilizes a superimposed multi-sinusoidal waveform and enables temporally resolved investigations on various electrochemical processes; however, the signal-to-noise ratio must be maximized while retaining linearity and stationarity within the system. In the present work, we probe the impact of the waveform’s total amplitude as well as the waveform and phase profiles. The equal amplitude multi-sinusoidal waveform with 35 mV total amplitude (<1 mV per frequency) and random phase profile exhibits the lowest impedance noise. This input signal is sufficient in avoiding the high frequency signal attenuation from the potentiostat’s low-pass filter. Additionally, we demonstrate the optimized dEIS waveform’s utility in investigating surface passivation within thin-film and composite silicon (Si) anodes. Within a two-electrode Si-lithium metal coin cell, the charge transfer resistance (RCT) associated with alloying kinetics dominates the overall impedance. RCT increases with state of charge indicating a kinetic bottleneck at higher lithiation states. To explore this further, we utilize a three-electrode Si-LiNi0.8Mn0.1Co0.1O2 pouch cell to deconvolute the Si impedance contributions. The evolution of the solid electrolyte interphase (SEI) and charge transfer resistances correlate to specific Li–Si alloy phases. We attribute this to the influence of Si particle volume expansion on SEI instabilities and a kinetic bottleneck at higher lithiation states.

Lam, Steven [ORNL]↗
DOE OSTI2022

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities in and around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and cooling (HVAC) system and responds to a demand response (DR) program. Despite the non-linear structure of the problem, we show how optimal solutions can be obtained efficiently using linear programming. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US, under both typical and atypical weather conditions. Finally, the results show that depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

HVAC↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗
DOE OSTI2025

Genetic tools for engineering Zymomonas mobilis , Cereibacter sphaeroides and Novosphingobium aromaticivorans to improve production of bioenergy compounds

Limited genetic tools for non-model bacteria are one of the limiting factors for genetic studies. This review compiles genetic tools used for three non-model alpha-proteobacteria, such as Zymomonas mobilis, Cereibacter (Rhodobacter) sphaeroides, and Novosphingobium aromaticivorans, which hold significant potential to produce industrially essential bioenergy compounds due to their distinctive metabolic pathways and resilience in extreme environments. Each of these strains has a unique genetic profile that enables them to efficiently carry out key reactions relevant to producing bioenergy compounds, such as converting sugars into bioenergy compounds and breaking down lignotoxins. Genetic tools can further optimize these strains for enhanced bioenergy compound production. This review explores the metabolic advantages of these organisms. It highlights the available array of genetic toolkits that can be shared among them to unlock their full potential for sustainable biofuel production.

Biofuel↗
DOE OSTI2024

Quantum annealing-aided design of an ultrathin-metamaterial optical diode

Abstract Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal–dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of Organic Electron Materials using Neutron Scattering (Final Report)

Neutron scattering is a powerful suite of measurement techniques that enable unique analysis of structure and dynamics of a wide variety of materials. Specifically, neutrons interact strongly with protons, which enables excellent contrast in light element organic materials that cannot be obtained with any other measurements. This series of three proposals focused on using inelastic neutron scattering (INS) to study the vibrations (dynamics) of the organic molecules involved in organic electronic devices. Our main goals were to demonstrate that INS is capable of elucidating new, useful, and unique knowledge about organic electronic samples that could be used to improve the function of devices or to solve outstanding problems that could not be solved using other measurement techniques. Since all of the goal involved using INS or other neutron techniques to do something “new” a large part of the effort was focused on complex data interpretation and development of modeling workflows that could be used by other members of the organic electronics community to study their materials. To facilitate the goal of making INS measurements more accessible to a broader community and for a wider range of materials, we optimized and engineered the computational workflows to be well documented, publicly available, and user friendly. The individual titles of the three sequential DOE-BES awards are: (1) Engineering doping profiles in organic semiconducting materials, (2) Measurement and modeling of molecular dopant structure and dynamics, and (3) Quantification of Dynamic Disorder in Electronic and Excitonic Organic Materials. For readability, this report is organized to highlight the progress of the research thematically. Many of the ideas started in one grant period and then spilled over into later awards and collaborative research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Li-Ion Battery Thermal Characterization for Thermal Management Design

Battery design efforts often prioritize enhancing the energy density of the active materials and their utilization. However, optimizing thermal management systems at both the cell and pack levels is also key to achieving mission-relevant battery design. Battery thermal management systems, responsible for managing the thermal profile of battery cells, are crucial for balancing the trade-offs between battery performance and lifetime. Designing such systems requires accounting for the multitude of heat sources within battery cells and packs. This paper provides a summary of heat generation characterizations observed in several commercial Li-ion battery cells using isothermal battery calorimetry. The primary focus is on assessing the impact of temperatures, C-rates, and formation cycles. Moreover, a module-level characterization demonstrated the significant additional heat generated by module interconnects. Characterizing heat signatures at each level helps inform manufacturing at the design, production, and characterization phases that might otherwise go unaccounted for at the full pack level. Further testing of a 5 kWh battery pack revealed that a considerable temperature non-uniformity may arise due to inefficient cooling arrangements. To mitigate this type of challenge, a combined thermal characterization and multi-domain modeling approach is proposed, offering a solution without the need for constructing a costly module prototype.

25 ENERGY STORAGE↗
DOE OSTI2020

Recoveries of Oil and Hydrolyzed Sugars from Corn Germ Meal by Hydrothermal Pretreatment: A Model Feedstock for Lipid-Producing Energy Crops

Vegetable oil is extracted from oil rich seeds, such as soybeans. Genetic engineering of green plants to accumulate oil in vegetative tissue is a future source of oil that promises increased land productivity and the use of marginal lands. However, the low concentration of lipids in current engineered plant biomass samples makes the oil extraction process challenging and expensive. In this study, liquid hot water (LHW) pretreatment was investigated to enhance oil recovery from the solids and increase enzymatic hydrolysis efficiency of such feedstocks. Corn germ meal was chosen as a model feedstock representing lipid-producing energy crops. Germ meal was pretreated at 160 and 180 °C for 10 and 15 min at 20% w/w solids loading. Enzymatic hydrolysis on the pretreated solid was performed. After pretreatment, the oil concentration increased by 2.2 to 4.2 fold. The most severe pretreatment condition of LHW, at 180 °C for 15 min, gave the maximum oil concentration (9.7%, w/w), the highest triacylglycerol (TAG) content of the extracted oil (71.6%), and the highest conversions of glucose and xylose (99.0% and 32.8%, respectively). This study demonstrates that the optimal pretreatment condition for corn germ meal is 180 °C LHW for 15 min. Pretreatment improves lipids recovery from oil bearing biomass with little or no effect on the lipid profile.

09 BIOMASS FUELS↗
DOE OSTI2021

Using MALDI-FTICR-MS Imaging to Track Low-Molecular-Weight Aromatic Derivatives of Fungal Decayed Wood

Low-molecular-weight (LMW) aromatics are crucial in meditating fungal processes for plant biomass decomposition. Some LMW compounds are employed as electron donors for oxidative degradation in brown rot (BR), an efficient wood-degrading strategy in fungi that selectively degrades carbohydrates but leaves modified lignins. Previous understandings of LMW aromatics were primarily based on “bulk extraction”, an approach that cannot fully reflect their real-time functions during BR. Here, we applied an optimized molecular imaging method that combines matrix-assisted laser desorption ionization (MALDI) with Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) to directly measure the temporal profiles of BR aromatics as Rhodonia placenta decayed a wood wafer. We found that some phenolics were pre-existing in wood, while some (e.g., catechin-methyl ether and dihydroxy-dimethoxyflavan) were generated immediately after fungal activity. These pinpointed aromatics might be recruited to drive early BR oxidative mechanisms by generating Fenton reagents, Fe2+ and H2O2. As BR progressed, ligninolytic products were accumulated and then modified into various aromatic derivatives, confirming that R. placenta depolymerizes lignin. Together, this work confirms aromatic patterns that have been implicated in BR fungi, and it demonstrates the use of MALDI-FTICR-MS imaging as a new approach to monitor the temporal changes of LMW aromatics during wood degradation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Abstract. Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.

Spuler, Scott M.↗