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At least 415 records · Page 23

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NASA NTRS2023

Digital Engineering Design Center (DEDC): Modelling an ISRU System

The DEDC provides immersive project-based learning on digital engineering toolsets and processes supporting NASA’s digital transformation goals: - Digital Engineering uses authoritative sources of systems' data and models as a continuum across disciplines to support integrated digital approach life cycle activities from concept through disposal. - The digital environment provided includes the state-of-the-art digital engineering suite, Siemens Xcelerator. The pilot project is developing an end-to-end integrated model of an In-Situ Resource Utilization (ISRU) system for commodities production: - ISRU uses local resources to provide mission consumables to enable a sustainable Moon or Mars surface presence. - The final digital twin product will include a methanation reactor, condenser, and electrolyzer subsystem.

Digital Engineering Design Center↗

DOE OSTI2022

Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Laplace's method applied to functional integrals

In statistical thermodynamics, partition functions are required to evaluate the current state. It is typically impossible to analytically calculate the resulting configuration integrals, but it is sometimes possible to obtain approximations in the limit of steep potential energies. This approach directly leverages Laplace’s method for approximating integrals and has been quite successful in recent applications. Motivated by models with an infinite number of degrees of freedom, this approach should be extended to the statistical thermodynamics of fields. To that end, Laplace’s method is developed for functional integrals.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗

NASA NTRS2017

Approach to Modeling Boundary Layer Ingestion Using a Fully Coupled Propulsion-RANS Model

Airframe-propulsion integration concepts that use boundary layer ingestion have the potential to reduce aircraft fuel burn. One concept that has been recently explored is NASA's Starc-ABL aircraft configuration, which offers the potential for 12% mission fuel burn reduction by using a turbo-electric propulsion system with an aft-mounted electrically driven boundary layer ingestion propulsor. This large potential for improved performance motivates a more detailed study of the boundary layer ingestion propulsor design, but to date, analyses of boundary layer ingestion have used uncoupled methods. These methods account for only aerodynamic effects on the propulsion system or propulsion system effects on the aerodynamics, but not both simultaneously. This work presents a new approach for building fully coupled propulsive-aerodynamic models of boundary layer ingestion propulsion systems. A 1D thermodynamic cycle analysis is coupled to a RANS simulation to model the Starc-ABL aft propulsor at a cruise condition and the effects variation in propulsor design on performance are examined. The results indicates that both propulsion and aerodynamic effects contribute equally toward the overall performance and that the fully coupled model yields substantially different results compared to uncoupled. The most significant finding is that boundary layer ingestion, while offering substantial fuel burn savings, introduces throttle dependent aerodynamics effects that need to be accounted for. This work represents a first step toward the multidisciplinary design optimization of boundary layer ingestion propulsion systems.

Boundary Layer Ingestion↗

NASA NTRS2017

Towards a New Food System Assessment: AgMIP Coordinated Global and Regional Assessments of Climate Change

Agricultural stakeholders need more credible information on which to base adaptation and mitigation policy decisions. In order to provide this, we must improve the rigor of agricultural modelling. Ensemble approaches can be used to address scale issues and integrated teams can overcome disciplinary silos. The AgMIP Coordinated Global and Regional Assessments of Climate Change and Food Security (CGRA) has the goal to link agricultural systems models using common protocols and scenarios to significantly improve understanding of climate effects on crops, livestock and livelihoods across multiple scales. The AgMIP CGRA assessment brings together experts in climate, crop, livestock, economics, and food security to develop Protocols to guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including, socioeconomic development, greenhouse gas concentrations, and specific pathways of agricultural sector development. Through these approaches, AgMIP partners around the world are providing an evidence base for their stakeholders as they make decisions and investments.

Ensembles↗

DOE OSTI2022

Model-based interface design for smart field-device integration

Operational complexity is ever-increasing for electric utilities that face challenges including integration of DERs, customer expectation of energy choices, the proliferation of non-utility-owned resources, new business models with energy service providers, and new technology with IT/OT convergence. To maintain and improve the quality of operations, planning, and decision-making in general, utilities need to manage and navigate the complexity. Managing complexity requires a modular, scalable, and flexible solution. Connecting large amounts of DERs and introducing new services requires increased grid control and evolving applications. In this paper, we show an approach utilizing model-based standardized interfaces that simplifies integration and deployment of new algorithms and smart field devices for interoperability across legacy or new systems. A modular design is presented for a reference implementation of widely used DNP3 and IEEE 2030.5 interfaces within an open-source, standards-based data integration platform for integration of smart field devices with independently developed, best-of-breed applications.

Model-driven development, system integration, smar↗

DOE OSTI2026

Testing Scale-Dependent Modified Gravity with DESI DR1

The Dark Energy Spectroscopic Instrument (DESI) provides an unprecedented opportunity to test deviations from general relativity (GR) that introduce a new physical scale within its redshift range. Using the connection between a Yukawa-like potential and the Hu-Sawicki $f(R)$ model, we place strong constraints on the range of a hypothetical fifth force mediated by a massive scalar field. We analyze the power spectrum measurements from DESI Data Release 1 using a baseline EFT model that employs the fkpt approach for the loop integrals. We find no evidence for deviations from GR and obtain the constraint $\log_{10} |f_{R_0}| < -4.59$ (95% C.L.). This corresponds to an upper bound at redshift zero on the scale at which corrections to GR become important, $λ< 17.81$ Mpc, or equivalently, a lower bound on the mass of the additional gravitational mediator of $m_ϕ> 3.60 \times 10^{-31}$ eV. We find that the modified gravity parameter $f_{R_0}$ is largely orthogonal to the cosmological parameters in the model, such that no additional projection effects relative to the GR case are introduced in this Full-Shape analysis. Furthermore, a second modified gravity parameter, the power index $n$, which modulates the time-variation of the associated mass, is found to be consistent with previous analyses that fixed it to unity. Adding DESI BAO data or other cosmological probes does not significantly change these results. The conclusions remain similar if the background evolution is described by evolving dark energy instead of a cosmological constant. Additionally, we test the robustness of the baseline model by varying the maximum wavenumber used in the Full-Shape analysis and analyzing the DESI targets separately. Finally, we analyze the degeneracies between the modified-gravity parameters and the sum of neutrino masses.

Gonzalez, D. [Guanajuato U.; UNAM, CCF] (ORCID:000↗

NASA NTRS2015

NASA's International Space Station: A Testbed for Planetary Protection Protocol Development

Wherever humans go, they inevitably carry along the critters that live in and on them. Conventional wisdom has long held that it is unlikely those critters could survive the space environment, but in 2007 some microscopic aquatic animals called Tardigrades survived exposure to space and in 2008 Cyanobacteria lived for 548 days outside the ISS. Unlike the Mars rovers that were cleaned once and sent on their way, crew members will provide a constantly regenerating contaminant source. Are we prepared to certify that we can meet forward contamination protocols as we search for life at new destinations? What about the organisms we might reasonably expect a crewed spacecraft to leak or vent? Do we even know what they are? How long might our tiny hitch-hikers survive in close proximity to a warm spacecraft that periodically leaks/vents water or oxygen and how might they mutate with long-duration exposure? How will these contaminants migrate from their source in conditions encountered in space or on other planetary surfaces? This project aims to answer some of these questions by bringing together key stakeholder communities to develop a human forward contamination test, analysis, and integration plan. A system engineering approach to identify the experiments, analysis, and modeling needed to develop the contamination control protocols required will be used as a roadmap to integrate the many different parts of this problem - from launch to landing, living, and working on another planetary surface.

Bell, M. S.↗

NASA NTRS2015

Nasa's International Space Station: A Testbed for Planetary Protection Protocol Development

Bell, M. S.↗

DOE OSTI2021

Stratigraphically Controlled Stress Variations at the Hydraulic Fracture Test Site-1 in the Midland Basin, TX

We investigated the relationship between stratigraphy, stress, and microseismicity at the Hydraulic Fracture Test Site-1. The site comprises two sets of horizontal wells in the Wolfcamp shale and a deviated well drilled after hydraulic fracturing. Regional stresses indicate normal/strike-slip faulting with E-W compression. Stress measurements in vertical and horizontal wells show that the minimum principal stress varies with depth. Strata with high clay and organic content show high values of the least compressive stress, consistent with the theory of viscous stress relaxation. By integrating data from core, logs, and the hydraulic fracturing stages, we constructed a stress profile for the Wolfcamp sequence, which predicts how much pressure is required for hydraulic fracture growth. We applied the results to fracture orientation data from image logs to determine the population of pre-existing faults that are expected to slip during stimulation. We also determined microseismic focal plane mechanisms and found slip on steeply dipping planes striking NW, consistent with the orientations of potentially active faults predicted by the stress model. This case study represents a general approach for integrating stress measurements and rock properties to predict hydraulic fracture growth and the characteristics of injection-induced microseismicity.

microseismicity↗

DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗

NASA NTRS2019

Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk analysis for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating over the intervals of time within which a fault occurs, accounting for an infinite number of cases. The response of the spacecraft to different faults can result in different schedules of activities, changing the intervals of integration. Europa currently uses models of spacecraft systems and components to simulate individual flight scenarios. The goal is to develop a framework for integrating, automating, and improving this modeling process. We describe an approach to generating the schedules for the different fault cases and determining the intervals for faults. It is not enough to just simulate individual cases because we are working with continuous variables that generate an infinite number of possible futures. Instead, we determine time windows within which certain faults can occur and use these time windows as bounds for integration. We found that determining these time windows is a constraint optimization problem. In order to represent these problems, we employ a language based on ontologies of behavior and scenarios. The language enables us to specify constraints in a simple, declarative syntax. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. For example, we created a detailed model of power generation, power use, and the corresponding effects on the battery in order to determine when an undervoltage fault can occur. An undervoltage during a trajectory correction maneuver requires that thrusting be interrupted for just enough time to recharge the battery such that the maneuver can be completed within battery limits. This behavior is generated based on the model to minimize the interruption time. For certain scenarios the constraint optimization problems were simple enough to be solved by hand, but the framework made the process substantially faster. It also produced solutions to other problems that we could not solve by hand or with existing tools and allowed us to generate and run many scenarios at once. The scenario language and engine greatly simplified the process of identifying time bounds and separating cases.

Everline, Chester J.↗

NASA NTRSDate not supplied

Dynamics and Clearance Analysis of NASA's Space Launch System Block-1 and Block 1B Solid Rocket Booster Separation Event

NASA’s Space Launch System (SLS) solid rocket booster separation event is an essential area of study requiring high fidelity modelling to capture the complexity of an intra-atmospheric stage separation event. The setup and analysis for this event leveraged the NASA-developed CLVTOPS multi-body dynamics toolchain to model aerodynamics at the separation event, booster thrust tailoff, and Core Stage engine throttling in addition to the separation system itself (booster separation motors (BSMs), pyrotechnic bolts and struts). The approach documented here will give a brief background on the CLVTOPS toolchain, how key input models are integrated and verified, and how results are quantified using an ordered-statistics approach. Specific areas discussed herein address the performance-to-orbit realized by adjusting the delay time between the separation cue and the actual separation event, and how the orientation of the aft BSMs was tuned to address small clearances between the aft diagonal attach struts and the Core Stage. Post-flight results from the inaugural Artemis I flight are also shown, and validation is performed between predicted vs. actual separation dynamics and clearances. Challenging night launch conditions necessitated comparisons that were more qualitative in nature, but still showed very good agreement to pre-flight predictions.

Carole J Addona↗

DOE OSTI2020

The value of seasonal energy storage technologies for the integration of wind and solar power

Energy storage at all timescales, including the seasonal scale, plays a pivotal role in enabling increased penetration levels of wind and solar photovoltaic energy sources in power systems.

Guerra, Omar J.↗

NASA NTRS2014

Modeling for Integrated Science Management and Resilient Systems Development

Many physiological, environmental, and operational risks exist for crewmembers during spaceflight. An understanding of these risks from an integrated perspective is required to provide effective and efficient mitigations during future exploration missions that typically have stringent limitations on resources available, such as mass, power, and crew time. The Human Research Program (HRP) is in the early stages of developing collaborative modeling approaches for the purposes of managing its science portfolio in an integrated manner to support cross-disciplinary risk mitigation strategies and to enable resilient human and engineered systems in the spaceflight environment. In this talk, we will share ideas being explored from fields such as network science, complexity theory, and system-of-systems modeling. Initial work on tools to support these explorations will be discussed briefly, along with ideas for future efforts.

Shelhamer, M.↗

DOE OSTI2022

SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Advanced reactors often involve complicated thermal-fluid (T-F) phenomena. Modeling such phenomena with the traditional one-dimensional (1-D) system code is a challenging task. The System Analysis Module (SAM), a modern nuclear system code, has developed a coarse mesh multi-dimensional (multi-D) flow model to capture the spatial effect of T-F phenomena in advanced reactors. As a coarse mesh solver, constitutive relations are required for SAM's multi-D model for unresolved fine-scale physics, such as turbulence. Here this work presents a novel approach that integrates neural networks as data-driven closure for SAM's multi-D flow model. The data-driven closure is trained with fine-resolution data to ensure its accuracy while maintaining a coarse mesh setup to ensure its efficiency and consistency with SAM. We demonstrate the applicability of this SAM-ML capability in an open volume thermal stratification problem, where a neural network model serves as the eddy viscosity closure. A customized interface between the neural network and SAM is developed to ensure flexible and efficient data exchange. The SAM-ML results demonstrate superior performance compared to SAM's built-in zero-equation eddy viscosity closure. The case study shows that although the generalization capability of the data-driven closure still needs to be improved for different transient case or different geometric setup, SAM -ML demonstrates good potential for challenging simulation problems with improved accuracy and computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗