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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 sources NASA NTRS DOE OSTI

NASA NTRS2003

Optimization of single-base-pair mismatch discrimination in oligonucleotide microarrays

The discrimination between perfect-match and single-base-pair-mismatched nucleic acid duplexes was investigated by using oligonucleotide DNA microarrays and nonequilibrium dissociation rates (melting profiles). DNA and RNA versions of two synthetic targets corresponding to the 16S rRNA sequences of Staphylococcus epidermidis (38 nucleotides) and Nitrosomonas eutropha (39 nucleotides) were hybridized to perfect-match probes (18-mer and 19-mer) and to a set of probes having all possible single-base-pair mismatches. The melting profiles of all probe-target duplexes were determined in parallel by using an imposed temperature step gradient. We derived an optimum wash temperature for each probe and target by using a simple formula to calculate a discrimination index for each temperature of the step gradient. This optimum corresponded to the output of an independent analysis using a customized neural network program. These results together provide an experimental and analytical framework for optimizing mismatch discrimination among all probes on a DNA microarray.

NASA Discipline Life Sciences Technologies↗

NASA NTRS1993

Reliability Technology to Achieve Insertion of Advanced Packaging (RELTECH) program

A joint military-commercial effort to evaluate multichip module (MCM) structures is discussed. The program, Reliability Technology to Achieve Insertion of Advanced Packaging (RELTECH), has been designed to identify the failure mechanisms that are possible in MCM structures. The RELTECH test vehicles, technical assessment task, product evaluation plan, reliability modeling task, accelerated and environmental testing, and post-test physical analysis and failure analysis are described. The information obtained through RELTECH can be used to address standardization issues, through development of cost effective qualification and appropriate screening criteria, for inclusion into a commercial specification and the MIL-H-38534 general specification for hybrid microcircuits.

Fayette, Daniel F.↗

NASA NTRS1992

Ka-band active MMIC subarray development

A 4 x 8 array consisting of two adjacent 4 x 4 MMIC subarray modules under development as part of a MMIC insertion program for the ACTS communication experiments is presented. The subarray features a high-density 'tile' configuration integrating 16 MMIC power amplifiers and 16 MMIC phase shifters. The radiating elements are cavity-backed, aperture coupled, linearly polarized microstrip patches arranged on a 4 x 4 square grid. Each module also incorporates a MMIC controller to set the phase shifts and to turn individual amplifiers 'on' or 'off'. The novel module construction technique is expected to establish a benchmark in Ka-band and higher-frequency array architectures. The hybrid integration approach used for the module can be replaced by multifunctional chips and multilayer distribution boards. The higher level of integration is to permit the dense grid spacing required for wide-angle scanning and reduced size, weight, and cost for the array.

Lee, Richard↗

NASA NTRS1998

Three Program Architecture for Design Optimization

In this presentation, I would like to review historical perspective on the program architecture used to build design optimization capabilities based on mathematical programming and other numerical search techniques. It is rather straightforward to classify the program architecture in three categories as shown above. However, the relative importance of each of the three approaches has not been static, instead dynamically changing as the capabilities of available computational resource increases. For example, we considered that the direct coupling architecture would never be used for practical problems, but availability of such computer systems as multi-processor. In this presentation, I would like to review the roles of three architecture from historical as well as current and future perspective. There may also be some possibility for emergence of hybrid architecture. I hope to provide some seeds for active discussion where we are heading to in the very dynamic environment for high speed computing and communication.

Miura, Hirokazu↗

NASA NTRS1999

Modeling of polychromatic attenuation using computed tomography reconstructed images

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

NASA Discipline Musculoskeletal↗

DOE OSTI2022

Optimal management of electric hotel loads in mild hybrid heavy duty truck

Here, the problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. Here, this work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2005

The Role of the Heavy Ions in the Generation of EMIC Waves

The effect of EMIC waves, generated by a positive ion temperature anisotropy on Earth s RC dynamics is one of the best known examples of wave-particle interaction in the magnetosphere and the most controversial mechanism of RC losses. Under certain conditions, relativistic electrons with energy greater than or equal to 1 MeV can be removed from the outer RB by EMIC wave scattering during a magnetic storm much faster than by any other loss mechanisms. That is why the calculation of EMIC waves are very critical part of the LWS program. Systematic studies of magnetosphere-plasmasphere-ionosphere coupling are needed in order to provide EMIC waves forecast on a global scale and include WPI processes in the RB modeling. To quantify the EMIC wave effects on the RC-ion and RB-electron dynamics, a self-consistent theoretical description of the ions, electrons, and EMIC waves should be employed in future RB studies. This talk will emphasize the role of the heavy ions in the EMIC waves formation on a global scale and their nonlinear coupling with lower hybrid waves.

Khazanov, G. V.↗

NASA NTRS2008

FPGA Coprocessor for Accelerated Classification of Images

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

Pingree, Paula J.↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2011

Mirror Technology Roadmap for NASA's Exoplanet Exploration Program

There are several possible approaches to designing exoplanet missions: (1) Coronagraphs (2) Interferometers (3) Starshades Wavefront sensing and control is the central concern, not mirror size (1) Starlight suppression with deformable mirrors (2) Thermal and structural stability (3) Metrology for sensing and control Diffraction-limited optical primary mirrors 4-m or larger are needed to detect Earthlike planets (1) Surface figure similar to HST required (2) Smaller primary mirrors can be used with aggressive coronagraph designs, but the stability tolerances become the driving concern (3) Stability tolerances of coronagraphs are greatly reduced when larger primaries are used in conjunction with 8th-order masks Long term vision for large telescope development includes space-based segmented-mirror telescopes using actively-controlled glass segments or silicon carbide hybrid-mirror designs

technology↗

DOE OSTI2022

SPEL: Software tool for Porting E3SM Land Model with OpenACC in a Function Unit Test Framework

Most high-end computers adopt hybrid architecture, porting a large-scale scientific code onto accelerators is necessary. The paper presents a generic method for porting large-scale scientific code onto accelerators using compiler directives within a modularized function unit test platform. We have implemented the method and designed a software tool (SPEL) to port the E3SM Land Model (ELM) onto the GPUs in the Summit computer. SPEL automatically generates GPU-ready test modules for all ELM functions, such as CanopyFlux, SoilTemperature, and EcosystemDynamics. SPEL breaks the ELM into a collection of standalone unit test programs for easy code verification and further performance improvement. We further optimize several ELM test modules with advanced techniques, including memory reduction, reconstructed parallel loops, and asynchronous GPU kernel launch. We hope our study will inspire new toolkit developments that expedite large-scale scientific code porting with compiler directives.

Schwartz, Peter↗

NASA NTRS1979

Terrestrial/land mobile satellite considerations, NASA plans and critical issues

This paper briefly reviews the land mobile market and discusses services that might be provided by hybrid terrestrial/land mobile satellite systems (T/LMSS). In view of the expected urban/suburban patterns of planned terrestrial systems it is suggested that a satellite may prove a cost-effective complement to terrestrial systems for serving thinly populated or large geographical areas. A basic Land Mobile Satellite Service (LMSS) is described, example applications are provided, and the status and rationale for LMSS frequencies in the 806-890 MHz band is given. Next, examples are given of NASA supported land mobile satellite experiments and a preliminary T/LMSS work plan, part of an overall Narrowband Program, is presented for structuring future work. The initiation of the T/LMSS plan depends on pending management/budget decisions. The report closes with a listing of issues and potential problem areas, the solutions of which NASA believes are critical to the success of the proposed T/LMSS effort.

Knouse, G. H.↗

NASA NTRS2013

Validation of Helicopter Gear Condition Indicators Using Seeded Fault Tests

A "seeded fault test" in support of a rotorcraft condition based maintenance program (CBM), is an experiment in which a component is tested with a known fault while health monitoring data is collected. These tests are performed at operating conditions comparable to operating conditions the component would be exposed to while installed on the aircraft. Performance of seeded fault tests is one method used to provide evidence that a Health Usage Monitoring System (HUMS) can replace current maintenance practices required for aircraft airworthiness. Actual in-service experience of the HUMS detecting a component fault is another validation method. This paper will discuss a hybrid validation approach that combines in service-data with seeded fault tests. For this approach, existing in-service HUMS flight data from a naturally occurring component fault will be used to define a component seeded fault test. An example, using spiral bevel gears as the targeted component, will be presented. Since the U.S. Army has begun to develop standards for using seeded fault tests for HUMS validation, the hybrid approach will be mapped to the steps defined within their Aeronautical Design Standard Handbook for CBM. This paper will step through their defined processes, and identify additional steps that may be required when using component test rig fault tests to demonstrate helicopter CI performance. The discussion within this paper will provide the reader with a better appreciation for the challenges faced when defining a seeded fault test for HUMS validation.

Dempsey, Paula↗

NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

DOE OSTI2025

Direct NeTS sampling of nuclear graphite $S(α, β, T)$ in Serpent

For advanced reactor applications, Neural Thermal Scattering (NeTS) modules were developed to predict the thermal scattering law (TSL or $S(α, β, T)$) of a nuclear graphite neutron moderator. NeTS are multi-layer, feedforward artificial neural networks, which act as universal function approximators designed for TSL datasets. In this case, a 4-layer neural network with 164 neurons per layer is trained using FLASSH evaluated data in PyTorch and serialized as a torchscript dictionary to predict $S(α, β, T)$ on-the-fly. Relative, absolute and maximum percent deviations of NeTS from File 7 data generated using the FLASSH code are on the order of 0.01%, 0.1% and 1%, respectively, with low inference latencies of 0.000172 s per $S(α, β, T)$ at a given temperature. Capturing the full dimensionality of possible inelastic neutron-lattice interactions, NeTS functionality is embedded in the Serpent Monte Carlo code, where $S(α, β, T)_{NeTS}$ sampling is conducted on-the-fly and compared to ACE look-up-tables for predicting TREAT criticality. k-eff differences between sampling algorithms of 6 pcm are observed and are within the order of Monte Carlo uncertainty. Compared to discrete and continuous-energy ACE files (30 MB and 131 MB per temperature), the NeTS format is on the order of 200–300 kB for a continuous-temperature, interpolation-free representation of $S(α, β, T)$ and cross sections. NeTS-in-Serpent runtimes comparable with ACE look-up tables are achieved by scaling NeTS for high performance computing architectures with hybrid OpenMP + MPI parallelization. This work validates a novel, self-contained reactor physics framework for predictive cross sections, and demonstrates a general methodology for embedding modern machine learning libraries within existing neutronic analysis frameworks.

Nuclear Criticality Safety Program (NCSP)↗

NASA NTRSDate not supplied

NASA’s Electric Aircraft Propulsion Research: Yesterday, Today and Tomorrow

NASA has been making investments since ~2015 in technologies related to electric aircraft propulsion. These investments span all-electric with our four passenger X-plane and electric vertical lift studies, to regional flight demonstrators and targeted technology maturation programs. These latter two areas are focused ultimately on reducing fuel burn and overall energy use in transport-class aircraft, with the goal of reducing carbon impact of aviation on our planet. Key technology contributions include such as electric machines, power electronics, cables/bus bars, fault management systems, controls and systems studies, and enabling materials. Today we are seeing the fundamental technology investments manifest themselves in flight demonstrations, that are aimed at impacting aircraft entering service 2035-2040 time range. These efforts have largely been aimed at megawatt scale technologies that can enable hybrid electric or mildly distributed airplane concepts. While these concepts offer benefits to regional and single isle aircraft it is thought that a more fully electrified propulsion system requiring greater than 10 MW of distributed power offers more possible pathways to configure the propulsion-airframe system to gain new efficiencies. A few examples of this are NASA’s SUSAN distributed electrofan concept and NASA University Leadership Initiatives such as CHEETA and IZEA that champion turbo-electric concepts. These concepts utilize combination of advanced technologies such as, fuel cells, power dense electronics and power dense electric machines and superconducting technologies. How much or which of these concepts will be adopted by industry is unclear, however another step function in electrifying aircraft propulsion is now on the horizon.

Electric Aircraft Propulsion↗

NASA NTRS1992

PARAS program: Phased array radio astronomy from space

An orbiting radio telescope is proposed which, when operated in a Very Long Baseline Interferometry (VLBLI) scheme, would allow higher (than currently available) angular resolution and dynamic range in the maps, and the ability of observing rapidly changing astronomical sources. Using a passive phases array technology, the proposed design consists of 656 hexagonal modules forming a 150 meter diameter dish. Each observatory module is largely autonomous, having its own photovoltaic power supply and low-noise receiver and processor for phase shifting. The signals received by the modules are channeled via fiber optics to the central control computer in the central bus module. After processing and multiplexing, the data is transmitted to telemetry stations on the ground. The truss frame supporting each observatory pane is a hybrid structure consisting of a bottom graphite/epoxy tubular triangle and rigidized inflatable Kevlar tubes connecting the top observatory panel and bottom triangle. Attitude control and stationkeeping functions are performed by a system of momentum wheels in the bus and four propulsion modules located at the compass points on the periphery of the observatory dish. Each propulsion module has four monopropellant thrusters and six hydrazine arcjets, the latter supported by a nuclear reactor. The total mass of the spacecraft is 22,060 kg.

Jakubowski, Antoni K.↗

DOE OSTI2024

Requesting an Exemption from Standard Compliance: EPAct State and Alternative Fuel Provider Fleet Program Guidance Document

The U.S. Department of Energy established the Alternative Fuel Transportation Program (Program) and associated regulatory requirements pursuant to the Energy Policy Act of 1992. The Program, otherwise known as the State and Alternative Fuel Provider Fleet Program, requires covered state government and alternative fuel provider fleets operating under Standard Compliance to acquire alternative fuel vehicles (AFVs) as a specific percentage of their annual non-excluded light-duty vehicle acquisitions. The opportunity for covered fleets operating under Standard Compliance to request exemptions from their AFV-acquisition requirements serves as administrative relief in the unlikely event a fleet is unable to satisfy its requirements through the normally available compliance alternatives. These alternatives include the acquisition of light-duty AFVs, the acquisition of other, creditable vehicles (e.g., gasoline-fueled hybrid electric vehicles), making certain investments, the purchase of biodiesel for use in medium- or heavy-duty vehicles to the maximum extent allowed, and purchasing or trading for banked AFV credits. This document addresses requests for exemptions from the AFV-acquisition requirements to help covered fleets better understand: How to file a request for an exemption, information and documentation DOE needs to process an exemption request, and important policies relevant for filing exemption requests.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗