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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 sourcesNASA NTRSDOE OSTI
NASA NTRS1986

The Extreme Ultraviolet Explorer and the local interstellar medium

The EUV Explorer (EUVE) mission is described, with emphasis on the overall capabilities of the instrumentation and the relevance of the mission to determining parameters of the local interstellar medium. The primary purpose of the mission is to search the celestial sphere for astronomical sources of EUV radiation (100-1000 A). The search will be accomplished with the use of three EUV telescopes, sensitive to different segments of the EUV band. A fourth telescope will perform a high-sensitivity search of a limited sample of the sky in the shortest-wavelength bands. The all-sky survey will be carried out in the first six months of the mission. The second six months of the mission, conducted entirely by Guest Investigators selected by NASA, will be devoted to spectroscopic observations of selected EUV sources. The instrumentation is now well developed. The best mirror has full-width/half-energy spread of 8 arcsec and a surface finish of 20 A. Prototype thin-film bandpass filters have been flown on the Space Shuttle and their performance optimized. Prototype detectors have been developed which have 600 by 600-pixel imaging capability and up to 80-percent quantum efficiency in parts of the EUV band.

Bowyer, Stuart↗
NASA NTRS2018

Structural Certification of Human-Rated Inflatable Space Structures

This paper details the results of an initial study to develop a certification plan for human-rated inflatable space structures, including guidelines for qualification testing. Habitable softgoods inflatables are multi-layered shell structures that use high-strength webbing, cordage and broadcloth fabric to carry the skin loads of a variety of volumetric shapes and structural architectures. The primary objectives of this study are to define the key parameters that affect these structures and propose a statistically robust approach to defining safety and knockdown factors based on test and analysis. Current NASA standards for habitable inflatable space structures use a factor of safety of 4, which was inherited from airship design criteria. An updated approach to defining a design factor, taking into account material strength variability, load variability in the article, number of test samples, and damage and degradation effects is specified. Accurate analytical modeling of these structures is hindered by the difficulty of obtaining accurate and consistent material data due to load-history- dependent, nonlinear load versus strain behavior. A building block approach to certification is detailed that uses stochastic modeling and statistical test design and analysis to address the unique challenges these high-strength softgoods structures present. Human-rated inflatable modules are a transformative capability for launching much larger habitable volumes into space than is possible with rigid shell structures. This research aims to provide the framework for certifying these structures for future human space exploration missions.

Jones, Thomas C.↗
NASA NTRSDate not supplied

Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) – Physical Workload Approximation

The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a hybrid reality exploration surface analog, “Assessments of Physiology and Cognition in Hybrid-reality Environments” (APACHE). The goal of APACHE is to create a planetary extravehicular activity (EVA) simulation environment that provides a representative physical and cognitive workload approximation using a combination of virtual reality (VR), physical reality, and hybrid reality (HR) techniques. To develop and characterize the physical workload approximation within the APACHE environment, a two-part approach was implemented. In part 1, baseline physical work load during ambulation within APACHE was evaluated and compared with that in other planetary EVA analog environments and with existing data sets from Apollo planetary EVAs and reduced gravity testing of prototype planetary spacesuits. For this evaluation, 10 subjects were asked to ambulate in three surface analog environments: a passive treadmill in APACHE, natural terrain in an outdoor field environment, and a standard motorized treadmill. Subjects’ heart rate and metabolic rate (VO2/VCO2) were measured and compared among the different test conditions and existing data sets. Gait parameters were also collected to compare with suited mechanics and to understand the role of gait kinematics in physical workload. In part 2, the aim is to evaluate the addition of a custom weighted body suit to the aforementioned surface analog environments and the ability to titrate the suit configuration to provide the best possible physical workload approximation for simulation of lunar and Martian EVAs.

Alex Baughman↗
DOE OSTI2024

Computational Exploration of High Entropy Alloys as Promising Materials for Future Beam Windows

With the ever-increasing demand for high beam power, the currently used beam-intercepting devices (BIDs) such as targets, and beam windows may not be able to handle the high power required for future accelerator complexes or the lifetime may be reduced drastically. As beam power increases, the damage incurred by BIDs, including thermal shock, fatigue, and irradiation damage, also rises. Therefore, it is imperative to design materials that can withstand high beam power for longer lifetimes. High entropy alloys (HEAs) have emerged as potential alternative materials for designing next-generation BIDs. In this study, we primarily focus on materials for developing beam windows for next-generation accelerator complexes. We propose an integrated approach that combines various computational techniques to study and design new materials. Specifically, we use CALPHAD, density functional theory (DFT), and molecular dynamics (MD) to comprehensively investigate the defect properties of suitable HEAs, offering potential alternatives for future beam windows. We begin by scanning the extensive phase space provided by Cr-Mn-V-Ti-Al-Co HEAs, selecting 8 compositions after evaluating approximately 120,000 unique compositions using CALPHAD. We, then employ DFT-informed machine learning techniques to develop force-field parameters. Finally, MD simulations using these developed force-field parameters will be used to study the effects of radiation damage on the defect and mechanical properties of the selected alloys. This research explains the use of the CALPHAD approach and shows how critical modeling (DFT and MD) is in developing novel material such as HEAs. It also highlights the promising role of machine learning in this field. The results from this study will greatly improve the novel materials development to be used in next-generation accelerator components, leading to higher beam power and longer operational times of BIDs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
NASA NTRS1998

An Object-Oriented Interface to the CCSDS Ground Telecommand Services

The Telecommand Data Routing and Channel Services defined by the Consultative Committee for Space Data Systems (CCSDS) are flexible enough to support a myriad of commanding models. Because the standard is so broad, the traditional approach has been to implement only the portion of the standard needed by the particular spacecraft being tested/operated. Tasked with providing Telecommand Services for an entire class of spacecraft, where each spacecraft may choose any valid CCSDS commanding model, NASA Code 584 designed a common architecture capable of handling the full CCSDS protocol. The solution uses another CCSDS standard - the Standard Formatted Data Unit (SFDU) as the interface to the Telecommand Services. SFDUs provide a consistent way of labelling data objects, as well as allowing data objects to encapsulate other data objects. The resulting interface is: - Flexible: The full Data Routing and Channel Services are available via a single interface. The client (i.e. the command source) may enter commands at any layer within the protocol stack, specify any of the data aggregation or segmentation methods, and dynamically set any configuration parameter defined in the standard. - Object-oriented: Each object specifies both the data and the actions to be performed with the data. An object may contain other objects. - Expandable: New capabilities are added by defining new objects. Objects pass thru the protocol layers until they reach the applicable layer. The resulting design is: - Modular: The logic for each protocol layer is contained in a separate Application Program Interface (API). The objects used for the external interface are also used for communication between layers. - Distributable: The design can be split along any layer boundary for distribution across multiple machines. The objects ensure data consistency across platforms. This paper describes the SFDU-based interface and the resulting protocol implementation. The implementation is currently used by NASA (National Aeronautics and Space Administration) for integration & test of the microwave Anisotropy Probe (MAP) and Earth observer-I (EO-l) spacecraft. It will be used for post-launch operations of these spacecraft as well as the Imager for Magnetopause to Aurora Global Exploration (IMAGE) spacecraft.

Ray, Timothy Joseph↗
NASA NTRS2016

Performance Efficient Launch Vehicle Recovery and Reuse

For decades, economic reuse of launch vehicles has been an elusive goal. Recent attempts at demonstrating elements of launch vehicle recovery for reuse have invigorated a debate over the merits of different approaches. The parameter most often used to assess the cost of access to space is dollars-per-kilogram to orbit. When comparing reusable vs. expendable launch vehicles, that ratio has been shown to be most sensitive to the performance lost as a result of enabling the reusability. This paper will briefly review the historical background and results of recent attempts to recover launch vehicle assets for reuse. The business case for reuse will be reviewed, with emphasis on the performance expended to recover those assets, and the practicality of the most ambitious reuse concept, namely propulsive return to the launch site. In 2015, United Launch Alliance (ULA) announced its Sensible, Modular, Autonomous Return Technology (SMART) reuse plan for recovery of the booster module for its new Vulcan launch vehicle. That plan employs a non-propulsive approach where atmospheric entry, descent and landing (EDL) technologies are utilized. Elements of such a system have a wide variety of applications, from recovery of launch vehicle elements in suborbital trajectories all the way to human space exploration. This paper will include an update on ULA's booster module recovery approach, which relies on Hypersonic Inflatable Aerodynamic Decelerator (HIAD) and Mid-Air Retrieval (MAR) technologies, including its concept of operations (ConOps). The HIAD design, as well as parafoil staging and MAR concepts, will be discussed. Recent HIAD development activities and near term plans including scalability, next generation materials for the inflatable structure and heat shield, and gas generator inflation systems will be provided. MAR topics will include the ConOps for recovery, helicopter selection and staging, and the state of the art of parachute recovery systems using large parafoils for space asset recovery and high altitude deployment. The next proposed HIAD flight demonstration is called HULA (for HIAD on ULA), and will feature a 6m diameter HIAD. An update for the HULA concept will be provided in this paper. As proposed, this demonstration will fly as a secondary payload on an Atlas mission. The Centaur upper stage provides the reentry pointing, deorbit burn, and entry vehicle spin up. The flight test will culminate with a recovery of the HIAD using MAR. HULA will provide data from a Low Earth Orbit (LEO) return aeroheating environment that enables predictive model correlation and refinement. The resultant reduction in performance uncertainties should lead to design efficiencies that are increasingly significant at larger scales. Relevance to human scale Mars EDL using a HIAD will also be presented, and the applicability of the data generated from both HULA and SMART Vulcan flights, and its value for NASA's human exploration efforts will be discussed. A summary and conclusion will follow.

Reed, John G.↗
NASA NTRS2017

Numerical Simulation of Illumination and Thermal Conditions at the Lunar Poles Using LOLA DTMs

We are interested in illumination conditions and the temperature distribution within the upper two meters of regolith near the lunar poles. Here, areas exist receiving almost constant illumination near areas in permanent shadow, which were identified as potential exploration sites for future missions. For our study a numerical simulation of the illumination and thermal environment for lunar near-polar regions is needed. Our study is based on high-resolution, twenty meters per pixel and 400 x 400 km large polar Digital Terrain Models (DTMs), which were derived from Lunar Orbiter Laser Altimeter (LOLA) data. Illumination conditions were simulated by synthetically illuminating the LOLA DTMs using the horizon method considering the Sun as an extended source. We model polar illumination for the central 50 x 50 km subset and use it as an input at each time-step (2 h) to evaluate the heating of the lunar surface and subsequent conduction in the sub-surface. At surface level we balance the incoming insolation with the subsurface conduction and radiation into space, whereas in the sub-surface we consider conduction with an additional constant radiogenic heat source at the bottom of our two-meter layer. Density is modeled as depth-dependent, the specific heat parameter as temperature-dependent and the thermal conductivity as depth- and temperature-dependent. We implemented a fully implicit finite-volume method in space and backward Euler scheme in time to solve the one-dimensional heat equation at each pixel in our 50 x 50 km DTM. Due to the non-linear dependencies of the parameters mentioned above, Newton's method is employed as the non-linear solver together with the Gauss-Seidel method as the iterative linear solver in each Newton iteration. The software is written in OpenCL and runs in parallel on the GPU cores, which allows for fast computation of large areas and long time scales.

Glaser, P.↗
DOE OSTI2025

Dark kinetic heating of exoplanets and brown dwarfs

Dark kinetic heating of neutron stars has been previously studied as a promising dark matter detection avenue. Kinetic heating occurs when dark matter is sped up to relativistic speeds in the gravitational well of high-escape velocity objects, and deposits kinetic energy after becoming captured by the object, thereby increasing its temperature. We show that dark kinetic heating can be significant even in objects with low-escape velocities, such as exoplanets and brown dwarfs, increasing the discovery potential of such searches. This can occur if there is a long-range dark force, creating a “dark escape velocity”, leading to heating rates substantially larger than those expected from neutron stars. We consequently set constraints on dark sector parameters using Wide-field Infrared Survey Explorer and JWST data on Super-Jupiter WISE 0855-0714, and map out future sensitivity to the dark matter scattering cross section below 10 −40 cm 2 . We compare dark kinetic heating rates of other lower escape velocity objects such as the Earth, Sun, and white dwarfs, finding complementary kinetic heating signals are possible depending on particle physics parameters.

Astrophysics↗
DOE OSTI2024

Power handling in a highly-radiative negative triangularity pilot plant

Abstract This work explores detailed power handling solutions for a class of high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept (Rutherford et al 2024 Plasma Phys. Control. Fusion ). The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only ∼50 cm (∼40% of the minor radius). The UEDGE code package is used for the boundary plasma solution to track plasma temperatures and fluxes to the divertor targets. It is found that for P SOL = 25 MW and n sep = 0.96 × 10 20 m −3 , conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For a fixed impurity fraction of just 0.13% Ne in the plasma, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW m −2 , while the electron temperature T e remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario, P SOL can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1% neon (Ne) impurity content, the divertor still experiences less than 10 MW m −2 at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550 ∘ C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01% Ne content.

Miller, M. A. (ORCID:0000000265406533)↗
DOE OSTI2023

Geothermal Play Fairway Analysis for Low-Temperature Resources in the Denver Basin

This dataset is part of an effort to highlight the advantages of incorporating low-temperature (< 150 C) geothermal resource evaluation into the implementation of combined heat and power (CHP), and geothermal direct use (GDU) technologies (e.g., space heating and/or cooling). For this Denver Basin example, resource favorability maps were created to identify potentially favorable areas for further geothermal exploration and are provided here. Favorability was based on three types of data: (1) geologic, (2) economic, and (3) risk. This raw data is also provided below. Geologic data include bottom-hole temperatures (BHT) from oil and gas wells, water co-production volumes from oil and gas wells, well groundwater levels, hot spring locations, temperatures, and chemistries, faults, and earthquakes. Economic feasibility data include population, thermal energy demand, infrastructure, and roads. Risk data (which includes data on excluded areas) include flood plains, protected lands (e.g. wildlife conservation areas, national parks). The included report describes this project in detail, covering workflows, relevant datasets, Python code, and both common and composite maps used to create low-temperature geothermal resource favorability maps for the Denver Basin, which extends across Colorado, Nebraska, and Wyoming. The figures in this report include: maps of the original datasets; maps of transformed data and derived parameters (such as the geothermal gradient or thermal conductivity); results of uncertainty analyses; results of data completeness (using the GeoRePORT tool); examples of the data combination and processing (using the geoPFA Python library, which is introduced in the attached report); favorability maps for each criteria; and a final combined favorability map. This project is designed to facilitate future deployment of CHP and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources in sedimentary basins.

15 GEOTHERMAL ENERGY↗
NASA NTRS2000

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗
NASA NTRS2011

Why Should Humans Explore a Near Earth Asteroid and What Factors Drive the Medical Risks?

The National Aeronautics and Space Administration (NASA) is currently considering plans for the human exploration of a Near Earth Asteroid (NEA). Reasons for undertaking the human exploration of a NEA include increasing the scientific understanding of the origins of our solar system, and developing technology for the exploration of more distant destinations such as Mars. Most mission scenarios have a duration on the order of several months or a year, most of which is spent in transit to and from the NEA. The choice of a particular NEA destination determines the mission duration and guides the types of exploration activities that can be performed on and near the NEA. NASA s Human Research Program (HRP) has identified short and long-term health risks associated with such missions and begun characterizing the level of risk. Some risk drivers are well known from missions to low Earth orbit and the Moon (e.g., the limited mass, volume, and power available for the medical care system). Other factors emerge as major drivers for NEA missions. Some are fundamental characteristics of the mission parameters (e.g., mission duration, distance) and others are strongly dependent on the specifics of how the mission is implemented (e.g., isolation and confinement). Careful consideration of these factors will be required for safe and effective missions to NEAs.

Kundrot, Craig E.↗
NASA NTRS2024

Assessing Acetone for the GISS ModelE2.1 Earth System Model

Acetone is an abundant volatile organic compound (VOC) in the atmosphere, with important influences on ozone and oxidation capacity. Direct sources include chemical production from other VOCs and anthropogenic emissions, terrestrial vegetation, biomass-burning emissions, and ocean production. Sinks include chemical loss, deposition onto the land surface, and ocean uptake. Acetone also has a lifetime that is long enough to allow transport and reactions with other compounds remote from its sources. The NASA Goddard Institute for Space Studies (GISS) Earth system model ModelE2.1 simulates a variety of Earth system interactions. Previously, acetone had a very simplistic representation in the ModelE chemical scheme. This study assesses a more sophisticated acetone scheme in which acetone is a full 3-dimensional tracer with explicit sources, sinks, and atmospheric transport. We first evaluate the new global acetone budget in the context of past literature. Estimated source and sink fluxes fall within the range of previous models, although total atmospheric burden and lifetime are at the lower end of the published literature. Acetone's new representation in ModelE2.1 also results in more realistic spatial and vertical distributions, which we compare against previous models and field observations. The seasonality of acetone-related processes was also studied in conjunction with field measurements, and these comparisons show promising agreement but also shortcomings at high-emission urban locations, where the model's resolution is too coarse to capture the true behavior. Finally, we conduct a variety of sensitivity studies that explore the influence of key parameters on the acetone budget and its global distribution. An impactful finding is that the production of acetone from precursor hydrocarbon oxidation has strong leverage on the overall chemical source, indicating the importance of accurate molar yields. Overall, our implementation is one that corroborates with previous studies and marks a significant improvement in the development of the acetone tracer in GISS ModelE2.1.

Acetone↗
DOE OSTI2022

CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum Algorithms (VQAs). VQAs rely upon the iterative optimization of a parameterized unitary circuit (ansatz) with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to classically choose the VQA ansatz initial parameters to be as close to optimal as possible to improve VQA accuracy and accelerate their convergence on today’s devices. This work tackles the problem of finding a good ansatz initialization, by proposing CAFQA, a Clifford Ansatz For Quantum Accuracy. The CAFQA ansatz is a hardware-efficient circuit built with only Clifford gates. In this ansatz, the parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation. The resulting initial states always equal or outperform traditional classical initialization (e.g., Hartree-Fock), and enable high-accuracy VQA estimations. CAFQA is well-suited to classical computation because: a) Clifford-only quantum circuits can be exactly simulated classically in polynomial time, and b) the discrete Clifford space is searched efficiently via Bayesian Optimization. For the Variational Quantum Eigensolver (VQE) task of molecular ground state energy estimation (up to 18 qubits), CAFQA’s Clifford Ansatz achieves a mean accuracy of nearly 99% and recovers as much as 99.99% of the molecular correlation energy that is lost in Hartree-Fock initialization. CAFQA achieves mean accuracy improvements of 6.4x and 56.8x, over the state-of-the-art, on different metrics. Here, the scalability of the approach allows for preliminary ground state energy estimation of the challenging chromium dimer (Cr2) molecule. With CAFQA’s high-accuracy initialization, the convergence of VQAs is shown to accelerate by 2.5x, even for small molecules. Furthermore, preliminary exploration of allowing a limited number of non-Clifford (T) gates in the CAFQA framework, shows that as much as 99.9% of the correlation energy can be recovered at bond lengths for which Clifford-only CAFQA accuracy is relatively limited, while remaining classically simulable.

bayesian optimization↗
NASA NTRS1999

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗
NASA NTRS2017

A Space-Based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21cm Global Spectrum

The redshifted 21 cm monopole is expected to be a powerful probe of the epoch of the first stars and galaxies(10 less than z less than 35). The global 21 cm signal is sensitive to the thermal and ionization state of hydrogen gas and thusprovides a tracer of sources of energetic photonsprimarily hot stars and accreting black holeswhich ionize andheat the high redshift intergalactic medium (IGM). This paper presents a strategy for observations of the globalspectrum with a realizable instrument placed in a low-altitude lunar orbit, performing night-time 40120 MHzspectral observations, while on the farside to avoid terrestrial radio frequency interference, ionospheric corruption,and solar radio emissions. The frequency structure, uniformity over large scales, and unpolarized state of theredshifted 21 cm spectrum are distinct from the spectrally featureless, spatially varying, and polarized emissionfrom the bright foregrounds. This allows a clean separation between the primordial signal and foregrounds. Forsignal extraction, we model the foreground, instrument, and 21 cm spectrum with eigenmodes calculated viaSingular Value Decomposition analyses. Using a Markov Chain Monte Carlo algorithm to explore the parameterspace defined by the coefficients associated with these modes, we illustrate how the spectrum can be measured andhow astrophysical parameters (e.g., IGM properties, first star characteristics) can be constrained in the presence offoregrounds using the Dark Ages Radio Explorer (DARE).

Burns, Jack O.↗
NASA NTRS2024

Effects of Water Limitation and Competition on Tree Carbon Allocation in an Earth System Modeling Framework

Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality-based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality-based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture. We used two models, LM3-PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3-PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two-dimensional trait space under different precipitation and mortality scenarios. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought-tolerance). Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3-PPA vs. BiomeE) and uncalibrated vs. calibrated BiomeE versions. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality-based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.

vegetation demographic model↗