Search NASA⌕ Search

SEARCH · Search NASA

Results for “future prospects”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Celestial Reference Frames at Multiple Radio Wavelengths

In 1997 the IAU adopted the International Celestial Reference Frame (ICRF) built from S/X VLBI data. In response to IAU resolutions encouraging the extension of the ICRF to additional frequency bands, VLBI frames have been made at 24, 32, and 43 gigahertz. Meanwhile, the 8.4 gigahertz work has been greatly improved with the 2009 release of the ICRF-2. This paper discusses the motivations for extending the ICRF to these higher radio bands. Results to date will be summarized including evidence that the high frequency frames are rapidly approaching the accuracy of the 8.4 gigahertz ICRF-2. We discuss current limiting errors and prospects for the future accuracy of radio reference frames. We note that comparison of multiple radio frames is characterizing the frequency dependent systematic noise floor from extended source morphology and core shift. Finally, given Gaia's potential for high accuracy optical astrometry, we have simulated the precision of a radio-optical frame tie to be approximately10-15 microarcseconds ((1-sigma) (1-standard deviation), per component).

quasars↗
NASA NTRS2019

Forecasting the Structure and Orientation of Earthbound Coronal Mass Ejections

Coronal Mass Ejections (CMEs) are the key drivers of strong to extreme space weather storms at the Earth that can have drastic consequences for technological systems in space and on ground.The ability of a CME to drive geomagnetic disturbances depends crucially on the magnetic structure of the embedded flux rope, which is thus essential to predict. The current capabilities in forecasting in advance(at least half a day before) the geo effectiveness of a given CME is however severely hampered by the lack of remote-sensing measurements of the magnetic field in the corona and adequate tools to predict how CMEs deform, rotate, and deflect during their travel through the coronal and interplanetary space as they interact with the ambient solar wind and other CMEs. These problems can lead not only to over estimation or underestimation of the severity of a storm, but also to forecasting "misses" and "false alarms" that are particularly difficult for the end-users. In this paper, we discuss the current status and future challenges and prospects related to forecasting of the magnetic structure and orientation of CMEs. We focus both on observational- and modeling-based (first principle and semiempirical) approaches and discuss the space- and ground-based observations that would be the most optimal for making accurate space weather predictions. We also cover the gaps in our current understanding related to the formation and eruption of the CME flux rope and physical processes that govern its evolution in the variable ambient solar windbackground that complicate the forecasting.

Kilpua, E. K. J.↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
NASA NTRS2024

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part II: OSEs and Results

The assimilation of data from hyperspectral infrared sounders in global data assimilation systems has historically been focused on observations in the longwave infrared (LWIR) region of the spectrum (650-1100 cm−1), despite the often concurrent availability of measurements from the shortwave infrared (SWIR) region of the spectrum (2150-2550 cm−1), because issues (like solar effects) have generally prevented the assimilation of SWIR observations. Recent advances in radiative transfer models have worked to address some of the previous challenges in simulating SWIR observations, and the assimilation of SWIR data (e.g. from potential future small-satellites) is now a feasible prospect. Still, a better understanding of how these observations perform in a data assimilation system and impact resulting analyses and NWP forecasts is necessary. In this study, the value of SWIR observations in global NWP is assessed by assimilating SWIR observations from the Cross-track Infrared Sounder (CrIS) in NOAA’s Global Data Assimilation System (GDAS). The methodologies used to enable the assimilation of these observations, including the implementation of a scene-dependent observation error and the enhancement of quality control procedures, are discussed, as are the results of Observing System Experiments (OSEs) conducted to evaluate the impact of assimilating SWIR observations on forecast skill. The overall results show that SWIR assimilation produces similar forecast impacts to LWIR assimilation. The ability to demonstrate that the assimilation or SWIR observations in NWP is a realistic prospect may help to shape future constellations of small-satellites to serve as a beneficial complement to the current constellation if hyperspectral IR sounders.

Erin Jones↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Current Status and Prospects of Solid-State Batteries as the Future of Energy Storage

Solid-state battery (SSB) is the new avenue for achieving safe and high energy density energy storage in both conventional but also niche applications. Such batteries employ a solid electrolyte unlike the modern-day liquid electrolyte-based lithium-ion batteries and thus facilitate the use of high-capacity lithium metal anodes thereby achieving high energy densities. Despite this promise, practical realization and commercial adoption of solid-state batteries remain a challenge due to the underlying material and cell level issues that needs to be overcome. This chapter thus covers the specific challenges, design principles and performance improvement strategies pertaining to the cathode, solid electrolyte and anode used in solid state batteries. Perspectives and outlook on specific applications that can benefit from the successful implementation of solid-state battery systems are also discussed. Overall, this chapter highlights the potential of solid-state batteries for successful commercial deployment in next generation energy storage systems.

Dixit, Marm↗
NASA NTRS1976

The dilemma of future electric power demand

Dim prospects are held out for continued exponential growth rates in the generation and use of electric power worldwide and in many local areas, extrapolating from current trends and common projections. While acknowledging the unique advantages of electric power use, the article points out the 30% level of efficiency in conversion of other forms of energy to electrical energy, with huge volumes of wasted energy plus thermal pollution. Even growth at a fixed rate is found problematical, with future needs exhausting water and land resources and fossil fuels in time. Alleviation of the situation by shifting much of the load to still unproven technologies is considered cautiously

Wu, Y.-C.↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Towards critical and supercritical electromagnetic fields

Abstract The availability of ever stronger, laser-generated electromagnetic fields underpins continuing progress in the study and application of nonlinear phenomena in basic physical systems, ranging from molecules and atoms to relativistic plasmas and quantum electrodynamics. This raises the question: how far will we be able to go with future lasers? One exciting prospect is the attainment of field strengths approaching the Schwinger critical field ${E}_{\mathrm{cr}}$ in the laboratory frame, such that the field invariant ${E}^2-{c}^2{B}^2>{E}_{\mathrm{cr}}^2$ is reached. The feasibility of doing so has been questioned, on the basis that cascade generation of dense electron–positron plasma would inevitably lead to absorption or screening of the incident light. Here we discuss the potential for future lasers to overcome such obstacles, by combining the concept of multiple colliding laser pulses with that of frequency upshifting via a tailored laser–plasma interaction. This compresses the electromagnetic field energy into a region of nanometre size and attosecond duration, which increases the field magnitude at fixed power but also suppresses pair cascades. Our results indicate that laser facilities with peak power of tens of PW could be capable of reaching ${E}_{\mathrm{cr}}$ . Such a scenario opens up prospects for the experimental investigation of phenomena previously considered to occur only in the most extreme environments in the universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Corrosion of Containment Alloys in Molten Salt Reactors and the Prospect of Online Monitoring

The aim of this review is to communicate some essential knowledge of the underlying mechanism of the corrosion of structural containment alloys during molten salt reactor operation in the context of prospective online monitoring in future MSR installations. The formation of metal halide species and the progression of their concentration in the molten salt do reflect containment corrosion, tracing the depletion of alloying metals at the alloy salt interface will assure safe conditions during reactor operation. Even though the progress of alloying metal halides concentrations in the molten salt do strongly understate actual corrosion rates, their prospective 1 st order kinetics followed by near-linearly increase is attributed to homogeneous matrix corrosion. The service life of the structural containment alloy is derived from homogeneous matrix corrosion and near-surface void formation but less so from intergranular cracking (IGC) and pitting corrosion. Online monitoring of corrosion species is of particular interest for molten chloride systems since besides the expected formation of chromium chloride species CrCl 2 and CrCl 3 , other metal chloride species such as FeCl 2 , FeCl 3 , MoCl 2 , MnCl 2 and NiCl 2 will form, depending on the selected structural alloy. The metal chloride concentrations should follow, after an incubation period of about 10,000 hours, a linear projection with a positive slope and a steady increase of <1 ppm per day. During the incubation period metal concentration show 1 st order kinetics and increasing linearly with time. Ideally, a linear increase reflects homogeneous matrix corrosion, while a sharp increase in the metal chloride concentration could set a warning flag for potential material failure within the projected service life, e.g. as result of intergranular cracking or pitting corrosion. Continuous monitoring of metal chloride concentrations can therefore provide direct information about the mechanism of the ongoing corrosion scenario and offer valuable information for a timely warning of prospective material failure.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1979

Problems and potentialities of cultured plant cells in retrospect and prospect

The past, present and expected future accomplishments and limitations of plant cell and tissue culture are reviewed. Consideration is given to the pioneering insights of Haberlandt in 1902, the development of culture techniques, and past work on cell division, cell and tissue growth and development, somatic embryogenesis, and metabolism and respiration. Current activity in culture media and technique development for plant regions, organs, tissues, cells, protoplasts, organelles and embryos, totipotency, somatic embryogenesis and clonal propagation under normal and space conditions, biochemical potentialities, and genetic engineering is surveyed. Prospects for the investigation of the induced control of somatic cell division, the division of isolated protoplasts, the improvement of haploid cell cultures, liquid cultures for somatic embryogenesis, and the genetic control of development are outlined.

Steward, F. C.↗
DOE OSTI2022

Freeze-in, glaciation, and UV sensitivity from light mediators

Dark matter (DM) freeze-in through a light mediator is an appealing model with excellent detection prospects at current and future experiments. Light mediator freeze-in is UV-insensitive insofar as most DM is produced at late times, and thus the DM abundance does not depend on the unknown early evolution of our universe. However the final DM yield retains a dependence on the initial DM population, which is usually assumed to be exactly zero. We point out that in models with light mediators, the final DM yield will also depend on the initial conditions assumed for the light mediator population. We describe a class of scenarios we call “glaciation” where DM freezing in from the SM encounters a pre-existing thermal bath of mediators, and study the dependence of the final DM yield on the initial temperature of this dark radiation bath. To compute DM scattering rates in this cosmology, we derive for the first time an exact integral expression for the Boltzmann collision term describing interactions between two species at different temperatures. We quantify the dependence of the DM yield on the initial dark temperature and find that it can be sizeable in regions near the traditional (zero initial abundance) freeze-in curve. We generalize the freeze-in curve to a glaciation band, which can extend as much as an order of magnitude below the traditional freeze-in direct detection target, and point out that the DM phase space distribution as well as the yield can be strongly dependent on initial conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

P not PQ

Parity solutions to the strong CP problem are a compelling alternative to approaches based on Peccei-Quinn symmetry, particularly given the expected violation of global symmetries in a theory of quantum gravity. The most natural of these solutions break parity at a low scale, giving rise to a host of experimentally accessible signals. We assess the status of the simplest parity-based solution in light of LHC data and flavor constraints, highlighting the prospects for near-future tests at colliders, tabletop experiments, and gravitational wave observatories. The origin of parity breaking and associated gravitational effects play crucial roles, providing new avenues for discovery through EDMs and gravity waves. These experimental opportunities underline the promise of generalized parity, rather than Peccei-Quinn symmetry, as a robust and testable solution to the strong CP problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy

DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L–H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ~8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1980

Design concepts for low-cost composite turbofan engine frame

Design concepts for low cost, lightweight composite engine frames were applied to the design requirements for the frame of a commercial, high bypass engine. Four alternative composite frame design concepts identified which consisted of generic type components and subcomponents that could be adapted to use in different locations in the engine and the different engine sizes. A variety of materials and manufacturing methods were projected with a goal for the lowest number of parts at the lowest possible cost. After a preliminary evaluation of all four frame concepts, two designs were selected for an extended design and evaluation which narrowed the final selection down to one frame that was significantly lower in cost and slighty lighter than the other frame. An implementation plan for this lowest cost frame is projected for future development and includes prospects for reducing its weight with proposed unproven, innovative fabrication techniques.

Mitchell, S. C.↗
NASA NTRS2002

Climate Change and Expected Impacts on the Global Water Cycle

How the elements of the global hydrologic cycle may respond to climate change is reviewed, first from a discussion of the physical sensitivity of these elements to changes in temperature, and then from a comparison of observations of hydrologic changes over the past 100 million years. Observations of current changes in the hydrologic cycle are then compared with projected future changes given the prospect of global warming. It is shown that some of the projections come close to matching the estimated hydrologic changes that occurred long ago when the earth was very warm.

Rind, David↗
NASA NTRS2003

On the Prospects of Measuring the Cosmic History of Element Synthesis with Future Far-IR/Submillimeter Observatories

To understand the cosmic history of element synthesis it will be important to obtain extinction-free measures of the heavy element contents of high-redshift objects and to chart two monumental events: the collapse of the first metal-free clouds to form stars, and the initial seeding of the universe with dust. The information needed to achieve these objectives is uniquely available in the far-infrared/submillimeter (FIR/SMM) spectral region. Following the Decadal Report and anticipating the development of the Single Aperature Far-IR (SAFIR) telescope capabilities of a large-aperature, background-limited FIR/SMM observatory and an interferometer on a boom, and discuss how such instruments could be used to measure the element synthesis history of the universe.

Leisawitz, David↗