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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Validation Of The Airspace Concept Evaluation System Using Real World Data

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

Zelinski, Shannon↗
NASA NTRS2008

Cooling Properties of the Shuttle Advanced Crew Escape Spacesuit: Results of an Environmental Chamber Experiment

The shuttle crew wears the Advanced Crew Escape Spacesuit (ACES) to protect themselves from cabin decompression and to support bail out during landing. ACES is cooled by a liquid-cooled garment (LCG) that interfaces to a heat exchanger that dumps heat into the cabin. The ACES outer layer is made of Gore-Tex(Registered TradeMark), permitting water vapor to escape while containing oxygen. The crew can only lose heat via insensible water losses and the LCG. Under nominal landing operations, the average cabin temperature rarely exceeds 75 F, which is adequate for the ACES to function. Problem A rescue shuttle will need to return 11 crew members if the previous mission suffers a thermal protection system failure, preventing it from returning safely to Earth. Initial analysis revealed that 11 crew members in the shuttle will increase cabin temperature at wheel stop above 80 F, which decreases the ACES ability to keep crew members cool. Air flow in the middeck of the shuttle is inhomogeneous and some ACES may experience much higher temperatures that could cause excessive thermal stress to crew members. Methods A ground study was conducted to measure the cooling efficiency of the ACES at 75 F, 85 F, and 95 F at 50% relative humidity. Test subjects representing 5, 50, and 95 percentile body habitus of the astronaut corps performed hand ergometry keeping their metabolic rate at 400, 600, and 800 BTU/hr for one hour. Core temperature was measured by rectal probe and skin, while inside and outside the suit. Environmental chamber wall and cooling unit inlet and outlet temperatures were measured using high-resolution thermistors ( 0.2 C). Conclusions Under these test conditions, the ACES was able to protect the core temperature of all test subjects, however thermal stress due to high insensible losses and skin temperature and skin heat flow may impact crew performance. Further research should be performed to understand the impact on cognitive performance.

Hamilton, Douglas↗
NASA NTRS2019

Preparing General Mission Analysis Tool for Operational Maneuver Planning of the Advanced Composition Explorer Mission

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

mission design↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Solar Anomalous and Magnetospheric Particle Explorer attitude control electronics box design and performance

The attitude Control Electronics (ACE) Box is the center of the Attitude Control Subsystem (ACS) for the Solar Anomalous and Magnetospheric Particle Explorer (SAMPEX) satellite. This unit is the single point interface for all of the Attitude Control Subsystem (ACS) related sensors and actuators. Commands and telemetry between the SAMPEX flight computer and the ACE Box are routed via a MIL-STD-1773 bus interface, through the use of an 80C85 processor. The ACE Box consists of the flowing electronic elements: power supply, momentum wheel driver, electromagnet driver, coarse sun sensor interface, digital sun sensor interface, magnetometer interface, and satellite computer interface. In addition, the ACE Box also contains an independent Safehold electronics package capable of keeping the satellite pitch axis pointing towards the sun. The ACE Box has dimensions of 24 x 31 x 8 cm, a mass of 4.3 kg, and an average power consumption of 10.5 W. This set of electronics was completely designed, developed, integrated, and tested by personnel at NASA GSFC. SAMPEX was launched on July 3, 1992, and the initial attitude acquisition was successfully accomplished via the analog Safehold electronics in the ACE Box. This acquisition scenario removed the excess body rates via magnetic control and precessed the satellite pitch axis to within 10 deg of the sun line. The performance of the SAMPEX ACS in general and the ACE Box in particular has been quite satisfactory.

Chamberlin, K.↗
NASA NTRS1999

Angiotensin-converting enzyme and matrix metalloproteinase inhibition with developing heart failure: comparative effects on left ventricular function and geometry

The progression of congestive heart failure (CHF) is left ventricular (LV) myocardial remodeling. The matrix metalloproteinases (MMPs) contribute to tissue remodeling and therefore MMP inhibition may serve as a useful therapeutic target in CHF. Angiotensin converting enzyme (ACE) inhibition favorably affects LV myocardial remodeling in CHF. This study examined the effects of specific MMP inhibition, ACE inhibition, and combined treatment on LV systolic and diastolic function in a model of CHF. Pigs were randomly assigned to five groups: 1) rapid atrial pacing (240 beats/min) for 3 weeks (n = 8); 2) ACE inhibition (fosinopril, 2.5 mg/kg b.i.d. orally) and rapid pacing (n = 8); 3) MMP inhibition (PD166793 2 mg/kg/day p.o.) and rapid pacing (n = 8); 4) combined ACE and MMP inhibition (2.5 mg/kg b.i.d. and 2 mg/kg/day, respectively) and rapid pacing (n = 8); and 5) controls (n = 9). LV peak wall stress increased by 2-fold with rapid pacing and was reduced in all treatment groups. LV fractional shortening fell by nearly 2-fold with rapid pacing and increased in all treatment groups. The circumferential fiber shortening-systolic stress relation was reduced with rapid pacing and increased in the ACE inhibition and combination groups. LV myocardial stiffness constant was unchanged in the rapid pacing group, increased nearly 2-fold in the MMP inhibition group, and was normalized in the ACE inhibition and combination treatment groups. Increased MMP activation contributes to the LV dilation and increased wall stress with pacing CHF and a contributory downstream mechanism of ACE inhibition is an effect on MMP activity.

Non-NASA Center↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
NASA NTRS1994

Inter-Agency Consultative Group for Space Science (IACG): Handbook of Missions and Payloads

The ACE spacecraft design is based on the Charge Composition Explorer (CCE) built by Johns Hopkins University (JHU) and the Applied Physics Lab (APL) for the AMPTE program. ACE is designed as a spinning spacecraft with its spin axis aligned to the Earth-Sun axis. The ACE launch weight will be approx. 633 kg, including 105 kg of scientific instruments and 184 kg of propellant. Using a Delta-class expendable launch vehicle, ACE will be launched into an L1 libration point (240 R(sub e)) orbit. Telemetry will be 6.7 kbps average, using tape recorder storage with daily readout to DSN. The experiment power requirement is approximately 76 W nominal and 96 W peak. The prime objective of the ACE mission is: (1) to determine accurate elemental and isotropic abundances including solar matter, local interstellar matter and local galactic matter; (2) to study the origin of elements and evolutionary processing in galactic nucleosynthesis, galactic evolution, origin and evolution of the solar system; (3) to study coronal formation and solar-wind acceleration processes; and (4) to study particle acceleration and transport, including coronal shock acceleration, stochastic flare acceleration, interplanetary shock acceleration, and interstellar acceleration and propagation. To accomplish this objective, ACE will perform comprehensive and coordinated determinations of the elemental and isotopic composition of energetic nuclei accelerated on the Sun, in interplanetary space, and from galactic sources. These observations will span five decades in energy, from solar wind to galactic cosmic ray energies, and will cover the element range from H-1 to Zr-40. Comparison of these samples of matter will be used to study the origin and subsequent evolution of both solar system and galactic material by isolating the effects of fundamental processes that include nucleosynthesis, charged and neutral particle separation, bulk plasma acceleration, and the acceleration of suprathermal and high-energy particles.

Source record↗
NASA NTRS2005

Environmental impact analysis with the airspace concept evaluation system

The National Aeronautics and Space Administration (NASA) Ames Research Center has developed the Airspace Concept Evaluation System (ACES), which is a fast-time simulation tool for evaluating Air Traffic Management (ATM) systems. This paper describes linking a capability to ACES which can analyze the environmental impact of proposed future ATM systems. This provides the ability to quickly evaluate metrics associated with environmental impacts of aviation for inclusion in multi-dimensional cost-benefit analysis of concepts for evolution of the National Airspace System (NAS) over the next several decades. The methodology used here may be summarized as follows: 1) Standard Federal Aviation Administration (FAA) noise and emissions-inventory models, the Noise Impact Routing System (NIRS) and the Emissions and Dispersion Modeling System (EDMS), respectively, are linked to ACES simulation outputs; 2) appropriate modifications are made to ACES outputs to incorporate all information needed by the environmental models (e.g., specific airframe and engine data); 3) noise and emissions calculations are performed for all traffic and airports in the study area for each of several scenarios, as simulated by ACES; and 4) impacts of future scenarios are compared to the current NAS baseline scenario. This paper also provides the results of initial end-to-end, proof-of-concept runs of the integrated ACES and environmental-modeling capability. These preliminary results demonstrate that if no growth is likely to be impeded by significant environmental impacts that could negatively affect communities throughout the nation.

Augustine, Stephen↗
NASA NTRS2006

Architecture Adaptive Computing Environment

Architecture Adaptive Computing Environment (aCe) is a software system that includes a language, compiler, and run-time library for parallel computing. aCe was developed to enable programmers to write programs, more easily than was previously possible, for a variety of parallel computing architectures. Heretofore, it has been perceived to be difficult to write parallel programs for parallel computers and more difficult to port the programs to different parallel computing architectures. In contrast, aCe is supportable on all high-performance computing architectures. Currently, it is supported on LINUX clusters. aCe uses parallel programming constructs that facilitate writing of parallel programs. Such constructs were used in single-instruction/multiple-data (SIMD) programming languages of the 1980s, including Parallel Pascal, Parallel Forth, C*, *LISP, and MasPar MPL. In aCe, these constructs are extended and implemented for both SIMD and multiple- instruction/multiple-data (MIMD) architectures. Two new constructs incorporated in aCe are those of (1) scalar and virtual variables and (2) pre-computed paths. The scalar-and-virtual-variables construct increases flexibility in optimizing memory utilization in various architectures. The pre-computed-paths construct enables the compiler to pre-compute part of a communication operation once, rather than computing it every time the communication operation is performed.

Dorband, John E.↗
NASA NTRS2011

The NASA Decadal Survey Aerosol, Cloud, Ecosystems Mission

In 2007, the National Academy of Sciences delivered a Decadal Survey (Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond) for NASA, NOAA, and USGS, which is a prioritization of future satellite Earth observations. The recommendations included 15 missions (13 for NASA, two for NOAA), which were prioritized into three groups or tiers. One of the second tier missions is the Aerosol, Cloud, (ocean) Ecosystems (ACE) mission, which focuses on climate forcing, cloud and aerosol properties and interactions, and ocean ecology, carbon cycle science, and fluxes. The baseline instruments recommended for ACE are a cloud radar, an aerosol/cloud lidar, an aerosol/cloud polarimeter, and an ocean radiometer. The instrumental heritage for these measurements are derived from the Cloudsat, CALIPSO, Glory, SeaWiFS and Aqua (MODIS) missions. In 2008, NASA HQ, lead by Hal Maring and Paula Bontempi, organized an interdisciplinary science working group to help formulate the ACE mission by refining the science objectives and approaches, identifying measurement (satellite and field) and mission (e.g., orbit, data processing) requirements, technology requirements, and mission costs. Originally, the disciplines included the cloud, aerosol, and ocean biogeochemistry communities. Subsequently, an ocean-aerosol interaction science working group was formed to ensure the mission addresses the broadest range of science questions possible given the baseline measurements, The ACE mission is a unique opportunity for ocean scientists to work closely with the aerosol and cloud communities. The science working groups are collaborating on science objectives and are defining joint field studies and modeling activities. The presentation will outline the present status of the ACE mission, the science questions each discipline has defined, the measurement requirements identified to date, the current ACE schedule, and future opportunities for broader community participation.

McClain, Charles R.↗
NASA NTRS2011

A Rocket-Base Study of Auroral Electrodynamics Within the Current Closure Ionosphere

The Auroral Current and Electrodynamics Structure (ACES) mission consisted of two sounding rockets launched nearly simultaneously from Poker Flat Research Range, AK on January 29, 2009 into a dynamic multiple-arc aurora. The ACES rocket mission, in conjunction with the PFISR Radar, was designed to observe the three-dimensional current system of a stable auroral arc system. ACES utilized two well instrumented payloads flown along very similar magnetic field footprints, at various altitudes with small temporal separation between both payloads. ACES High, the higher altitude payload (apogee 360 km), took in-situ measurements of the plasma parameters above the current closure region to provide the input signature into the lower ionosphere. ACES Low, the low-altitude payload (apogee 130 km), took similar observations within the current closure region, where cross-field currents can flow. We present results comparing observations of the electric fields, magnetic fields, electron flux, and the electron temperature at similar magnetic footpoints between both payloads. We further present data from all-sky imagers and PFISR detailing the evolution of the auroral event as the payloads traversed regions connected by similar magnetic footpoints. Current measurements derived from the magnetometers on both payloads are further compared. We examine data from both PFISR and observations on the high-altitude payload which we interpreted as a signature of electron acceleration by means of Alfv n waves. We further examine all measurements to understand ionospheric conductivity and how energy is being deposited into the ionosphere through Joule heating. Data from ACES is compared against models of Joule heating to make inferences regarding the effect of collisions at various altitudes.

Kaeppler, Stephen R.↗
DOE OSTI2024

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

Abstract Can the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Duncan, James P. C.↗