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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Implications of rootless geothermal models: Missing processes, parameter compensation, and imposter convection

Numerical models of geothermal systems commonly capture only the top of a reservoir. Deeper areas of the reservoir are simplified to a boundary at the base of the model domain. Commonly, the basal boundary is given either a heat source or a source of mass and enthalpy. Here we developed and present simple numerical experiments which demonstrate that these approaches do not produce the correct model behavior in comparison to a model that captures the entire convecting domain with a heat flux only. We describe a variety of incorrect types of model behavior that arise directly from the choice of boundary condition, independent of the specific parameterization of the model. Heat sources are sensitive to the thickness of the domain and parameters take unphysical values to compensate for the reduced height. The combined mass/heat boundary can produce temperatures that show similarities to circulating geothermal systems, but with incorrect fluid flow and a strong boundary layer focused at the base of the simulated clay cap. These errors likely cause parameters to adjust their values to compensate for the incorrect physics. We highlight these issues and show an example from a developed reservoir model. Initial calibrations to natural state temperature were unsuitable for history matching. The 3D model required parameter adjustments to achieve a more realistic production model. Appropriate mitigation measures should be considered to reduce parameter compensation and improve decision-support models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
NASA NTRS2002

Process Cost Modeling for Multi-Disciplinary Design Optimization

For early design concepts, the conventional approach to cost is normally some kind of parametric weight-based cost model. There is now ample evidence that this approach can be misleading and inaccurate. By the nature of its development, a parametric cost model requires historical data and is valid only if the new design is analogous to those for which the model was derived. Advanced aerospace vehicles have no historical production data and are nowhere near the vehicles of the past. Using an existing weight-based cost model would only lead to errors and distortions of the true production cost. This report outlines the development of a process-based cost model in which the physical elements of the vehicle are costed according to a first-order dynamics model. This theoretical cost model, first advocated by early work at MIT, has been expanded to cover the basic structures of an advanced aerospace vehicle. Elemental costs based on the geometry of the design can be summed up to provide an overall estimation of the total production cost for a design configuration. This capability to directly link any design configuration to realistic cost estimation is a key requirement for high payoff MDO problems. Another important consideration in this report is the handling of part or product complexity. Here the concept of cost modulus is introduced to take into account variability due to different materials, sizes, shapes, precision of fabrication, and equipment requirements. The most important implication of the development of the proposed process-based cost model is that different design configurations can now be quickly related to their cost estimates in a seamless calculation process easily implemented on any spreadsheet tool. In successive sections, the report addresses the issues of cost modeling as follows. First, an introduction is presented to provide the background for the research work. Next, a quick review of cost estimation techniques is made with the intention to highlight their inappropriateness for what is really needed at the conceptual phase of the design process. The First-Order Process Velocity Cost Model (FOPV) is discussed at length in the next section. This is followed by an application of the FOPV cost model to a generic wing. For designs that have no precedence as far as acquisition costs are concerned, cost data derived from the FOPV cost model may not be accurate enough because of new requirements for shape complexity, material, equipment and precision/tolerance. The concept of Cost Modulus is introduced at this point to compensate for these new burdens on the basic processes. This is treated in section 5. The cost of a design must be conveniently linked to its CAD representation. The interfacing of CAD models and spreadsheets containing the cost equations is the subject of the next section, section 6. The last section of the report is a summary of the progress made so far, and the anticipated research work to be achieved in the future.

Bao, Han P.↗
DOE OSTI2020

Incorporating physical constraints into Gaussian process surrogate models (LDRD Project Summary)

This report summarizes work done under the Laboratory Directed Research and Development (LDRD) project titled "Incorporating physical constraints into Gaussian process surrogate models?' In this project, we explored a variety of strategies for constraint implementations. We considered bound constraints, monotonicity and related convexity constraints, Gaussian processes which are constrained to satisfy linear operator constraints which represent physical laws expressed as partial differential equations, and intrinsic boundary condition constraints. We wrote three papers and are currently finishing two others. We developed initial software implementations for some approaches. This report summarizes the work done under this LDRD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-informed KNN milling stability model with process damping effects

This paper describes a k-nearest neighbors, or KNN, model for milling stability including process damping effects. A physics-based, frequency domain milling stability solution is used to generate the training data, but does not incorporate process damping effects. The data set is then updated using limited tests to capture the process damping behavior. A “stair step” approach is used to select the test points, where a first spindle speed-axial depth combination is selected based on the physics-based stability map, subsequent tests are defined using the previous test result, and data points are updated by knowledge of process damping behavior and the test results. Furthermore, the KNN modeling approach demonstrates the ability to predict both stable and unstable results, including process damping behavior.

42 ENGINEERING↗
NASA NTRS1994

A process improvement model for software verification and validation

We describe ongoing work at the NASA Independent Verification and Validation (IV&V) Facility to establish a process improvement model for software verification and validation (V&V) organizations. This model, similar to those used by some software development organizations, uses measurement-based techniques to identify problem areas and introduce incremental improvements. We seek to replicate this model for organizations involved in V&V on large-scale software development projects such as EOS and Space Station. At the IV&V Facility, a university research group and V&V contractors are working together to collect metrics across projects in order to determine the effectiveness of V&V and improve its application. Since V&V processes are intimately tied to development processes, this paper also examines the repercussions for development organizations in large-scale efforts.

Callahan, John↗
NASA NTRS1994

A process improvement model for software verification and validation

We describe ongoing work at the NASA Independent Verification and Validation (IV&V) Facility to establish a process improvement model for software verification and validation (V&V) organizations. This model, similar to those used by some software development organizations, uses measurement-based techniques to identify problem areas and introduce incremental improvements. We seek to replicate this model for organizations involved in V&V on large-scale software development projects such as EOS and space station. At the IV&V Facility, a university research group and V&V contractors are working together to collect metrics across projects in order to determine the effectiveness of V&V and improve its application. Since V&V processes are intimately tied to development processes, this paper also examines the repercussions for development organizations in large-scale efforts.

Callahan, John↗
DOE OSTI2021

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

42 ENGINEERING↗
NASA NTRS2022

Coevolution of Machine Learning and Process-Based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Saman Razavi↗
DOE OSTI2022

Process Model-Based Validation of the Intensification of Biomass Fast Pyrolysis in a Fluidized Bed via Autothermal Operation

A model for the fluidized-bed pyrolysis of biomass is extended to enable the simulation of intensified autothermal operation. In this system, partial oxidation of char and pyrolysate species replaces an external heat source to supply the enthalpy of pyrolysis, greatly increasing the throughput of a given fast pyrolysis reactor. Oxidation reactions are compiled from CRECK and other literature sources and extended to cover the species found in pyrolysis products, including a derivation of the catalytic effect of ash on char combustion from experimental studies at Iowa State University (ISU). Results indicate a roughly 3-fold increase in biomass throughput for a given reactor volume at the pilot scale with minimal loss of valuable products, in agreement with published data from the 3 × 10 -2 m 3 reactor operated by ISU. Additionally, a proposal for a 250 ton/day commercial-scale biomass pyrolysis reactor is analyzed, showing that the same size reactor operated by heating the fluidizing gas externally would have only one-tenth the capacity; if operated by heating the sand externally, the sand would need to circulate at an impractical 0.7 bed volume per minute rate to maintain a 250 ton/day biomass throughput.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Gaussian-process generative model for the QCD equation of state

We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian process regression method. We impose first-principles theoretical constraints from lattice quantum chromodynamics and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian process regression model to vary freely near the phase transition region, we generate random smooth crossover equations of state with different speeds of sound that do not rely on specific parametrizations. Here, we explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2011

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA) that were developed using the Aspen Custom Modeler and Aspen Plus process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

Allada, Rama Kumar↗
DOE OSTI2021

Divergent controls of soil organic carbon between observations and process-based models

Abstract The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori . While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.

58 GEOSCIENCES↗
NASA NTRS1996

The Role of Independent V&V in Upstream Software Development Processes

This paper describes the role of Verification and Validation (V&V) during the requirements and high level design processes, and in particular the role of Independent V&V (IV&V). The job of IV&V during these phases is to ensure that the requirements are complete, consistent and valid, and to ensure that the high level design meets the requirements. This contrasts with the role of Quality Assurance (QA), which ensures that appropriate standards and process models are defined and applied. This paper describes the current state of practice for IV&V, concentrating on the process model used in NASA projects. We describe a case study, showing the processes by which problem reporting and tracking takes place, and how IV&V feeds into decision making by the development team. We then describe the problems faced in implementing IV&V. We conclude that despite a well defined process model, and tools to support it, IV&V is still beset by communication and coordination problems.

Easterbrook, Steve↗
NASA NTRS2012

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA). These dynamic models were developed using the Aspen Custom Modeler (Registered TradeMark) and Aspen Plus(Registered TradeMark) process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

Allada, Rama Kumar↗
NASA NTRS2002

The Influence of Tropospheric Processes in Modeling the Middle Atmosphere with Gravity Waves

Our Numerical Spectral Model (NSM) extends from the ground up into the thermosphere and has a vertical grid point resolution of about 0.5 km to resolve the interactions of gravity waves (GWs) described with Hines' Doppler Spread Parameterization (DSP). This model produces in the stratosphere and mesosphere the major features of QBO, SAO, tides, and planetary waves. The purpose of this paper is to discuss results from an initial study with our 3D model that shows how certain tropospheric processes can affect the dynamics of the middle atmosphere. Under the influence of tropospheric heating, and augmented by GW interactions, two distinct but related processes can be identified. (1) A meridional circulation develops in the stratosphere, with rising motions at low latitudes that are in magnitude comparable to the downward propagation of the QBO. As Dunkerton pointed out, a larger GW source is then required to reproduce the observed QBO, which tends to move us closer to the values recommended for the DSP. This has significant consequences for our model results that describe the upper mesosphere, considering the general importance of GWs for this region and in influencing planetary waves (e.g., 2-day wave) and tides in particular. (2) Tropospheric heating produces zonal jets near the tropopause that are related to latitudinal variations in pressure and reversing temperature variations (resembling the dynamical conditions near the mesopause), which in turn is conducive to generate baroclinic instability. Modeling results show that our ability to generate the QBO critically depends on the magnitude of the temperature reversal that is a measure of this instability. Planetary waves are generated in this process, which can apparently interfere with or augment the GW interactions. As originally demonstrated by Lindzen and Holton, the eastward propagating Kelvin waves and westward propagating Rossby gravity waves (generated by tropospheric convection) can in principle provide the acceleration to influence the QBO, and we were able to confirm this with our 3D model.

Mayr, H. G.↗
NASA NTRS1981

Is energy storage and release part of the substorm process?

Models for magnetospheric substorms were considered. A modified model which includes the growth phase, a time interval prior to the onset of the expansion phase, during which energy was transferred from a solar wind to the magnetosphere and stored for subsequent release, is discussed. Evidence for energy storage in the tail prior to substorm expansion for both isolated and moderate substorm activity is reviewed.

Clauer, C. R.↗