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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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Interferometrically determined angular diameters obtained at the Palomar Testbed Interferometer (PTI) for 80 giant and supergiant stars are presented in this paper.
This paper presents a new formulation for spacecraft inertia estimation from test data. Specifically, the inertia estimation problem is formulated as a constrained least squares minimization problem with explicit bounds on the inertia matrix incorporated as LMIs [linear matrix inequalities). The resulting minimization problem is a semidefinite optimization that can be solved efficiently with guaranteed convergence to the global optimum by readily available algorithms. This method is applied to data collected from a robotic testbed consisting of a freely rotating body. The results show that the constrained least squares approach produces more accurate estimates of the inertia matrix than standard unconstrained least squares estimation methods.
Starlight suppression research is Stowed in Delta IV-H advancing rapidly to approach the required contrast ratio. The current analysis of the TPF Coronagraph system indicates that it is feasible to achieve the stability required by using developing technologies: a) Wave Front Sensing and Control (DMs, control algorithms, and sensing); b) Laser metrology. Yet needed: a) Property data measured with great precision in the required environments; b) Modeling tools that are verified with testbeds.
This slide presentation reviews the use of the High Contrast Imaging Testbed (HCIT) in the design and improvement of space coronagraphs. The objectives of the work are to: (1) Advance the technology readiness of space coronagraph hardware, techniques, algorithms, and predictive models; (2) Provide proof-of-concept demonstrations of coronagraph techniques; and (3) Support collaborations across the exoplanet community in pursuit of the optimal space coronagraph architecture.
An atmospheric sounding mission starts with a wide range of concept designs involving measurement technologies, observing platforms, and observation scenarios. Observing system simulation experiment (OSSE) is a technical approach to evaluate the relative merits of mission and instrument concepts. At Jet Propulsion Laboratory (JPL), the OSSE team has developed an OSSE environment that allows atmospheric scientists to systematically explore a wide range of mission and instrument concepts and formulate a science traceability matrix with a quantitative science impact analysis. The OSSE environment virtually creates a multi-platform atmospheric sounding testbed (MAST) by integrating atmospheric phenomena models, forward modeling methods, and inverse modeling methods. The MAST performs OSSEs in four loosely coupled processes, observation scenario exploration, measurement quality exploration, measurement quality evaluation, and science impact analysis.
For more than a half-century, several types of altitude-compensating rocket nozzles have been proposed and analyzed, but very few have been adequately tested in a relevant flight environment. One type of altitude-compensating nozzle is the dual-bell rocket nozzle, which was first introduced into literature in 1949. Despite the performance advantages that have been predicted, both analytically and through static test data, the dual-bell nozzle has still not been adequately tested in a relevant flight environment. This presentation proposes a method for conducting testing and research with a dual-bell rocket nozzle in a flight environment. We propose to leverage the existing NASA F-15 airplane and Propulsion Flight Test Fixture as the flight testbed, with the dual-bell nozzle operating during captive-carried flights, and with the nozzle subjected to a local flow field similar to that of a launch vehicle. The primary objective of this effort is not only to advance the technology readiness level of the dual-bell nozzle, but also to gain a greater understanding of the nozzle mode transitional sensitivity to local flow-field effects, and to quantify the performance benefits with this technology. The predicted performance benefits are significant, and may result in reducing the cost of delivering payloads to low-Earth orbit.
This presentation provides an overview of the Advanced Modular Power Systems (AMPS) Project, along with sections to describe the three technical focus areas - Modular Power Electronics & Standards, Autonomous Power Control, and the Modular Power Testbed.
The spacecraft testbed is a crucial part of the spacecraft development and operations phase. Given the aggressive schedule of the Mars Global Surveyor spacecraft, the Spacecraft Test Laboratory will play a crucial part during the MGS development phase in support of flight software testing, fault protection testing, sequence testing, and other areas to meet the November, 1996 launch date.
The Micro-Precision Control/Structure Interaction (CSI) program at JPL is chartered to develop the structures and control technology needed for sub-micron level stabilization of future optical space systems. The extreme dimensional stability required for such systems derives from the need to maintain the alignment and figure of critical optical elements to a small fraction (typically 1/20th to 1/50th) of the wavelength of detected radiation (about 0.5 micron for visible light, 0.1 micron for ultraviolet light). This requirement is common to a broad class of optical sysetms. The challenge for CSI arises when such systems become large, with spatially distributed optical elements mounted on lightweight, flexible structure. This paper will present an overview of the approach that is being taken by JPL's CSI program to address this challenge. In particular, the paper will discuss the application of CSI technology to a specific example of a future large optical space mission. Experimental demonstration of the technology on ground-based testbeds will also be presented.
The Principal Component-based Radiative Transfer Model Retrieval Algorithm (PCRTM-RA) for carbon monoxide (CO) retrieval has been improved for better use of National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. One of the explicit purposes of the campaign was to characterize wildfire-induced atmospheric changes. Coincidental measurements from various airborne instruments including NAST-I infrared hyperspectral measurements from the NASA ER-2 aircraft provided us an opportunity to test and improve the PCRTM-RA CO retrieval. By relaxing the vertical correlation of CO profiles in the a-priori covariance constraint, a significant improvement in the vertical structure of the CO retrieval has been confirmed. The methodology is validated using a synthetic testing dataset that covers observations associated with various CO vertical profiles including that of an extremely polluted atmosphere. The methodology is also applied to real NAST-I measurements, and the results have successfully demonstrated the capability of using PCRTM-RA retrieval results for CO plume evolution and transport monitoring.
Noncollinear ferroic materials are sought after as testbeds to explore the intimate connections between topology and symmetry, which result in electronic, optical, and magnetic functionalities not observed in collinear ferroic materials. For example, ferroaxial materials have rotational structural distortions that break mirror symmetry and induce chirality. When ferroaxial order is coupled with ferroelectricity arising from a broken inversion symmetry, it offers the prospect of electric-field-control of the ferroaxial distortions and opens up new tunable functionalities. However, chiral multiferroics, especially ones stable at room temperature, are rare. A strain-stabilized, room-temperature chiral multiferroic phase in single crystals of BaTiS 3 is reported here. Using first-principles calculations, the stabilization of this multiferroic phase having P6 3 space group for biaxial tensile strains exceeding 1.5% applied on the basal ab-plane of the room temperature P6 3 cm phase of BaTiS 3 is predicted. The chiral multiferroic phase is characterized by rotational distortions of TiS 6 octahedra around the long c-axis and polar displacement of Ti atoms along the c-axis. Furthermore, the ferroaxial and ferroelectric distortions and their domains in P6 3 -BaTiS 3 are directly resolved using atomic resolution scanning transmission electron microscopy. Landau-based phenomenological modeling predicts a strong coupling between the ferroelectric and the ferroaxial order making P6 3 -BaTiS 3 an attractive test bed for achieving electric-field-control of chirality.
Abstract In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal‐organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal‐organic chalcogenides (MOCs), which are an air‐stable, underexplored material class hosting room‐temperature excitons, can be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, the role of phonons in the optical transitions of the prototypical MOC [AgSePh] ∞ is elucidated. Impulsive stimulated Raman scattering (ISRS) allows the detection of coherent exciton oscillations driven by Fröhlich interaction with low‐energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton‐phonon coupling (Huang‐Rhys parameter ≈1.7) and its anharmonicity, manifested as a nontrivial temperature‐dependent Stokes shift. The ab initio calculations support these observations, hinting at an anharmonic behavior of the low‐energy phonons <200 cm −1 . These results untangle complex exciton‐phonon interactions in MOCs, establishing an ideal testbed for room‐temperature many‐body phenomena.
Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis–Hastings algorithm, using hydrogen migration to copper (111) surface as an example.
Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.
The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model. The challenge for applications that harness one of these exascale systems will be to avoid lock-in and to preserve performance portability. We report here on our results of using Kokkos to accelerate a real-world application on NERSC's Perlmutter Phase 1 (using NVIDIA A100 accelerators) and Crusher, the testbed system for OLCF's Frontier (using AMD MI250X). By porting to Kokkos, we successfully ran the same X-ray tracing code on both systems and achieved speed-ups between 13 % and 66 % compared to the original CUDA code. Finally, these results are a highly encouraging demonstration of using Kokkos to accelerate production science code.
Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO 2 reservoir model based on a potential CO 2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO 2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO 2 , and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO 2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO 2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.
Riverine dissolved organic carbon (DOC) is a critical biogeochemical component that transmits information from Arctic soils to the Arctic Ocean, significantly influencing carbon dynamics in this unique ecosystem. As DOC travels downstream, it undergoes transformations that alter its composition and fate. The Yukon River serves as an effective testbed for modelling these dynamics, offering sufficient scale to capture key biogeochemical processes while having a simpler hydrology than other major Arctic rivers, as well as long-term DOC observational data for model validation. To investigate DOC transformations during transit in the Yukon River, we adapted our Arctic Riverine Organic Macromolecular Model by applying regional-specific parameterisations. Our model simulates the transport and transformation of 15 organic macromolecules, including CDOM (coloured dissolved organic matter), proteins, polysaccharides, lipids, lignin phenols, and humic substances. Initial DOC concentrations were derived from observed soil organic carbon stocks in the surrounding watershed, while chemical transformations and hydrological dynamics were modelled along the river's course. Sensitivity and uncertainty analyses were conducted using a Monte Carlo approach under two experimental setups. Results revealed that variability in DOC and CDOM concentrations at the river mouth were predominantly driven by initial DOC concentration (~70% of variability explained) and dilution at confluence points (~10%). The refractory fraction of DOC explained 21%–88% of the variability in 14 macromolecular concentrations and ranked in the top five sensitive parameters for all outputs when a uniform parameter distribution was assumed. However, when a more likely variability was applied to this parameter, its influence on DOC and CDOM decreased. Given that refractory DOC accounts for ~80% of total DOC in Arctic Rivers, this suggests that most DOC resists degradation and retains its chemical composition during transport to the coastal environment. River velocity, which determines residence time, explained 8%–47% of the variability in protein, polysaccharide, lipid, pigments, and lignin phenols at the river mouth. In contrast, chemical turnover times contributed only 1%–5% to output variability. Our findings underscore the need for improved land-specific headwater observations, including seasonal soil moisture and lateral transport dynamics that control the initial tributary-specific DOC inputs. With accelerated permafrost thaw and increasing river discharge, extending our model to other Arctic River systems and seasons will enhance understanding of Arctic riverine carbon fluxes and their contributions to the Arctic Ocean.
Abstract The uncertainties associated with technology‐specific and geography‐specific degradation rates make it difficult to calculate the levelized cost of energy, and thus the economic viability of solar energy. In this regard, millions of fielded photovoltaic modules may serve as a global testbed, where we can interpret the routinely collected time series maximum power point (MPP) data to assess the time‐dependent “health” of solar modules. The existing characterization methods, however, cannot effectively mine/decode these datasets to identify various degradation pathways. In this paper, we propose a new methodology called the Suns‐Vmp method , which offers a simple yet powerful approach to monitoring and diagnosing time‐dependent degradation of solar modules by using the MPP data. The algorithm reconstructs “IV” curves by using the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. These synthetic IV characteristics are then used to determine the time‐dependent evolution of circuit parameters (eg, series resistance), which in turn allows one to deduce the dominant degradation modes (eg, solder bond failure) of solar modules. The proposed method has been applied to a test facility at the National Renewable Energy Laboratory. Our analysis indicates that the solar modules degraded at a rate of ~0.7%/year because of discoloration and weakened solder bonds. These conclusions are validated by independent outdoor IV measurements and on‐site imaging characterization. Integrated with physics‐based degradation models or machine learning algorithms, the method can also serve to predict the lifetime of photovoltaic systems.