SEARCH · Search NASA

Results for “explorations”

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.

SourceResearch connectionFrom yearThrough year

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

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Exploring Geothermal Potential of Great Basin Sub-Regions

exploration↗

DOE OSTI2024

Enterprise: Exploration of Concepts, Perspectives and Implications for Systems Engineering

The purpose of this paper is to explore the concept of ‘enterprise’ in the context of Systems Engineering (SE). The term ‘enterprise’ has been used extensively to generally describe large complex entities that have an extensive scope of operations. However, a deeper examination of ‘enterprise’ significance for SE can provide insights as our challenges continue with increasingly complex, uncertain, ambiguous, and integrated entities struggling to thrive in the future. The paper explores three central topics. First, the concept of enterprise is introduced as a central aspect of the future focus for SE, as recognized in the INCOSE SE Vision 2035. Second, a more detailed examination of the enterprise concept is developed in relationship to SE. The thrust of this examination is to understand the nature and role of ‘enterprise’ across a broad spectrum of literature and knowledge, ultimately providing a more informed perspective of enterprise for SE. As part of this exploration, a bibliometric analysis of the term ‘enterprise’ is performed. This exploration extracts key themes (clusters) in the ‘enterprise’ literature. Third, challenges for further development and inculcation of ‘enterprise’ within the SE discipline and support for realization of the SE 2035 Vision are suggested. These challenges point out the need to ‘think differently’ about ‘enterprise’ within the SE context. ‘Enterprise’ is proposed as a central, albeit different, perspective for the SE discipline. Finally, the paper closes with a first–generation perspective for ‘enterprise’ in pursuit of the SE Vision 2035.

42 ENGINEERING↗

DOE OSTI2021

The Gamow Explorer: a Gamma-Ray Burst Observatory to study the high redshift universe and enable multi-messenger astrophysics

The Gamow Explorer will use Gamma Ray Bursts (GRBs) to: 1) probe the high redshift universe (z < 6) when the first stars were born, galaxies formed and Hydrogen was reionized; and 2) enable multi-messenger astrophysics by rapidly identifying Electro-Magnetic (IR/Optical/X-ray) counterparts to Gravitational Wave (GW) events. GRBs have been detected out to z ~ 9 and their afterglows are a bright beacon lasting a few days that can be used to observe the spectral fingerprints of the host galaxy and intergalactic medium to map the period of reionization and early metal enrichment. Gamow Explorer is optimized to quickly identify high-z events to trigger follow-up observations with JWST and large ground-based telescopes. A wide field of view Lobster Eye X-ray Telescope (LEXT) will search for GRBs and locate them with arc-minute precision. When a GRB is detected, the rapidly slewing spacecraft will point the 5 photometric channel Photo-z Infra-Red Telescope (PIRT) to identify high redshift (z < 6) long GRBs within 100s and send an alert within 1000s of the GRB trigger. An L2 orbit provides < 95% observing efficiency with pointing optimized for follow up by the James Webb Space Telescope (JWST) and ground observatories. The predicted Gamow Explorer high-z rate is <10 times that of the Neil Gehrels Swift Observatory. The instrument and mission capabilities also enable rapid identification of short GRBs and their afterglows associated with GW events. The Gamow Explorer will be proposed to the 2021 NASA MIDEX call and if approved, launched in 2028.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Exploring strongly correlated quantum spin systems with quantum computers

At inception, quantum annealing leveraged quantum mechanics for classical optimization tasks. However, as machine coherence increases, the realization of quantum spin systems has emerged as an even more fruitful application of this computational model. Alternatives to the standard gate model deserve exploration, to achieve useful quantum advantage via analog quantum computing. The recent demonstration of so-called coherent quantum annealing with 1,000s of qubits, further expands the potential for this hardware to explore quantum system dynamics where the effects of quantum fluctuations can be carefully controlled and observed directly. Implementing existing celebrated spin models—or in fact new and dedicated ones—into quantum annealers will lead to observation and detection of quantum phenomena not yet observed, or not visualized directly, in experimental physics laboratories. Rather than computing or simulating quantum systems, these quantum computers allow one to simply build quantum systems, and experiment on them in an uniquely controlled way, with characterization down to the constitutive degree of freedom. This provides an unprecedented opportunity for understanding the physics of quantum spin systems. Keywords: Theoretical physics currently abounds of interesting theoretical spin models to explore frustration, strongly correlated spins, spin liquids, fractionalized excitations, and topological matter. However enticing, such models are generally only weak proxies for the properties of actual materials. In quantum annealers these models could be realized and experimented upon. Moreover, many more realistic models of such materials could be realized in quantum annealers. Employed in this way, quantum annealers provide an extraordinary versatile platform to explore quantum effects that are hard to find, detect, and characterize in natural materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the competitive antagonism effect in combined chloride-sulfate attack: A numerical exploration

Highlights: • A chemo-physical-mechanical method based on thermodynamic modelling is implemented. • The contradicting findings from reported experiments are numerically explored. • The competitive antagonism effect is time-dependent rather than a conclusive term. • The competitive antagonism effect is quantifiable with a novel assessment criterion. It is of tremendous difficulty to draw a conclusive verdict on the complex process of mutual interference regarding the combined chloride-sulfate attack on concrete, where many have explored the topic, but contradicting results have been reported. In this paper, by taking advantages of the chemo-physical-mechanical method, based on the robust technique of thermodynamic modelling, the competitive antagonism effect in combined chloride-sulfate attack is investigated. Firstly, a range of reported tests are numerically modelled to demonstrate the effectiveness of the employed method in interpreting those opposing and diverse experimental outcomes. More importantly, a fresh conjecture incorporating a novel assessment criterion for the competitive antagonism effect is proposed and supported through a carefully designed illustrative example. Based on the numerical exploration, unique findings and implications for future engineering practices are revealed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗

DOE OSTI2026

De-Risking Exploration for Geothermal Plays in Magmatic Environments Through Open-Source Tools: An Open-Source Python Framework for 2D and 3D Play Fairway Analysis

The De-Risking Exploration for Geothermal Plays in Magmatic Environments (DEEPEN) project seeks to accelerate superhot geothermal development by reducing exploration risk through advanced open-source modeling tools. This work presents a novel Python-based framework, geoPFA, for conducting 2D and 3D play fairway analysis (PFA) tailored to superhot geothermal systems. Building on previous methodologies, the framework integrates thermo-hydro-mechanical-chemical simulation outputs from TReactMech, resulting in improved representation of subsurface properties that are critical to superhot resource producibility. The workflow has been applied to the Nesjavellir field in Iceland, a candidate site for the third Iceland Deep Drilling Project's superhot production scenarios. This application demonstrates the value of modular, transparent, and extensible workflows for integrating geological, geophysical, and simulation-derived datasets in high-enthalpy environments. Preliminary results indicate favorable zones consistent with known hydrothermal activity and suggest possible upflow from the Hengill volcanic system. The geoPFA library is publicly available, offering a scalable and reproducible approach to geothermal exploration across varied geological contexts.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Exploring for Superhot Geothermal Targets in Magmatic Settings: 2022 Field Campaign at Newberry Volcano

This paper presents preliminary results from a subset of work carried out as part of a multinational research project entitled DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN), supported by the U.S. Department of Energy (DOE) and Geothermica, a joint effort by EU member states and associated countries. The DEEPEN project will develop a customized approach to exploration for supercritical and superhot geothermal plays in magmatic systems, which will be applied to two demonstration sites. This paper summarizes field activities carried out at the U.S. demonstration site, Newberry Volcano in central Oregon. The objective of this work effort is to refine the subsurface model of Newberry Volcano, with special focus on deeper zones including the magmatic plumbing system and other key geologic elements. New data collection included gravity and wideband magnetotelluric (MT) surveys, as well as reinstallation of a seismic network. The National Renewable Energy Laboratory (NREL) and Enthalpion Energy LLC (Enthalpion) worked with the Deschutes National Forest Fort Rock District to use a low ground disturbance method of MT deployment to collect MT data inside the caldera and other restricted areas inside the National Volcanic Monument. This opened these areas to geophysical exploration for the first time in decades. Sites along and adjacent to the south rim of the volcano constituted the primary survey objectives. A team from Lawrence Berkeley National Laboratory (LBNL), the U.S. Geological Survey (USGS), and AltaRock also began the process of reinstalling the seismic network from the AltaRock enhanced geothermal system (EGS) demonstration in anticipation of further development activities at the site. The data ingestion, reduction, and analysis phase of the project is ongoing. We are currently processing the MT and gravity data and are developing a new, highly GPU-accelerated, 3D joint MT and gravity inversion to better localize the south rim/south flank conductive target and better understand its relationship to deep heat, fluid sources, and surface extrusive features. Joint inversions, which have not yet been undertaken at Newberry, will allow us to obtain constraints on the geologic model that cannot be determined from each method in isolation, improving our ability to image key geologic features at depth.

geophysics↗

DOE OSTI2023

Exploring the Complex Chemistry of Uranium within Molten Chloride Salts

Harsh environments represent a unique opportunity to explore new frontiers in chemistry while developing novel tools to meet global needs. Exploring the chemistry of uranium within molten salts is a key example. Actinide chemistry within the highly ionic environment of a molten salt is poorly understood, particularly in the presence of common salt impurities or without active oxidation state control. Delving into this chemistry can provide new insight into actinide and f-electron interactions. Furthermore, expanding our chemical knowledge can also enable advances in and deployment of molten salt reactors or molten salt recycle schemes. Both molten salt applications aim towards providing green, reliable, and equitable energy as well as critical materials for the world. Here the utilization of visible absorbance and Raman spectroscopies to understand and quantify U within chloride-based salt eutectics is discussed. Furthermore, machine learning techniques in the form of chemometric modelling are developed and described, providing advanced analytical tools to quantify and characterize the U present. In conclusion, these tools are then leveraged to monitor and explore the dynamic fundamental chemistry of U within chloride-based salt melts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗

DOE OSTI2022

Deep reaction network exploration at a heterogeneous catalytic interface

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗

DOE OSTI2024

BGC Atlas: a web resource for exploring the global chemical diversity encoded in bacterial genomes

Secondary metabolites are compounds not essential for an organism’s development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35 000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Exploring experimental conditions to reduce uncertainties in the optical potential

Uncertainty quantification for nuclear theories has gained a more prominent role in the field, with more groups attempting to understand the uncertainties in their calculations. However, recent studies have shown that the uncertainties on the optical potentials are too large for the theory to be useful. The goal of this work is to explore possible experimental conditions that may reduce the uncertainties on elastic scattering and single-nucleon transfer cross sections that come from the fitting of the optical model parameters to experimental data. Using Bayesian methods, we explore the effect of the uncertainties of optical model parameters on the angular grid of the differential cross section, including cross-section data at nearby energies, and changes in the experimental error bars. We also study the effect on the resulting uncertainty when other observables are included in the fitting procedure, particularly the total (reaction) cross sections. We study proton and neutron elastic scattering on 48 Ca and 208 Pb. We explore the parameter space with the Markov-chain Monte Carlo method, produce posterior distributions for the optical model parameters, and construct the corresponding 95% confidence intervals on the elastic-scattering cross sections. We also propagate the uncertainties on the optical potentials to the 48 Ca(d,p) 49 Ca(g.s.) and 208 Pb(d,p) 209 Pb(g.s.) cross sections. We find little sensitivity to the angular grid and an improvement of up to a factor of 2 on the uncertainties by including data at a nearby energy. Although reducing the error bars on the data does reduce the uncertainty, the gain is often considerably smaller than one would naively expect. We also find that the inclusion of total reaction cross section can improve the uncertainty although the magnitude of the effect depends strongly on the cases considered.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Information-Theoretic Exploration of Multivariate Time-Varying Image Databases

Modern scientific simulations produce very large datasets, making interactive exploration of such data computationally prohibitive. An increasingly common data reduction technique is to store visualizations and other data extracts in a database. The Cinema project is one such approach, storing visualizations in an image database for post hoc exploration and interactive image-based analysis. This work focuses on developing efficient algorithms that can quantify various types of multivariate dependencies existing within multi-variable datasets. It applies specific mutual information measures for the quantification of salient regions from multivariate image data. Here, using such information measures, the opacity of the images is modulated so that the salient regions are automatically highlighted and the domain scientists can interactively explore the most relevant regions for scientific discovery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

AEDGE: Atomic Experiment for Dark Matter and Gravity Exploration in Space

We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity. This paper is based on a submission (v1) in response to the Call for White Papers for the Voyage 2050 long-term plan in the ESA Science Programme. ESA limited the number of White Paper authors to 30. However, in this version (v2) we have welcomed as supporting authors participants in the Workshop on Atomic Experiments for Dark Matter and Gravity Exploration held at CERN: ({\tt https://indico.cern.ch/event/830432/}), as well as other interested scientists, and have incorporated additional material.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗