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Results for “Parameter Identifiability”

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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydrocarbon-based membranes cost-effectively manage species transport and increase performance in thermally regenerative batteries

Low-temperature heat (T<130°C) can be utilized by thermally regenerative batteries (TRBs) for power production, allowing the thermal energy to be converted to storable chemical potential energy. However, TRBs suffer from high ohmic losses and ammonia crossover, which has slowed their development. In this study, we examined how the use of six different membranes influenced TRB performance, determined the most influential membrane parameters, and identified promising membrane candidates that cost-effectively increase TRB performance. Of the six membranes examined, an inexpensive, hydrocarbon CEM (Selemion CMVN) had low ammonia crossover without compromising resistance, resulting in good performance across all metrics studied. A thin anion exchange membrane (Sustainion, 50 microns) showed a high peak power density of 82 mW cm –2 due to low resistance, but the average power density and energy density were low due to high ammonia flux. Full discharge curves using Selemion CMVN provided an average power density of 26 ± 7 mW cm –2 with an energy density of 2.9 Wh L –1 , which were large improvements on previous TRBs. A techno-economic analysis showed that Selemion CMVN had the lowest levelized cost of storage ($410 per MWh) at an applied current density of 50 mA cm –2 .

25 ENERGY STORAGE↗
DOE OSTI2020

Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input–output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. In this paper, we first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Component-based SHGC determination of BIPV glazing for product comparison

Building-integrated photovoltaic (BIPV) systems are intrinsically designed to generate electricity and to provide at least one building-related function. When BIPV modules act as glazing products in windows, skylights or curtain walls, their ability to control the transmission of solar energy into the building must be characterised by a Solar Heat Gain Coefficient (SHGC) or g value (also known as Total Solar Energy Transmittance – TSET – or “solar factor”). For the comparison of BIPV glazing products consisting of one PV laminate and possibly further, conventional glazing layers separated by gas-filled cavities, the procedures documented in international standards for architectural glazing (e.g. ISO 9050 and EN 410) form a suitable starting point. Easily implemented modifications to these procedures are proposed to take both optical inhomogeneity (if relevant) and extraction of electricity from BIPV glazing units into account. Geometrically complex glazing and shading devices, and light-scattering glazing layers, are outside the scope of the proposed methodology; SHGC determination for obliquely incident solar radiation is also excluded. For these cases, the experimental calorimetric approach documented in [ISO 19467:2017; ISO 19467-2:2021] is recommended. The paper also presents results and conclusions from an implementation exercise and sensitivity study carried out by participants of the IEA-PVPS Task 15 on BIPV. The cell coverage ratio in the PV laminate, the thermal resistance offered by the glazing configuration, the choice of boundary conditions and the effect of extracting electricity were all identified as parameters which significantly affect the SHGC value determined for a given type of BIPV glazing. A practicable approach to accommodate the great variety of dimensions typical for BIPV glazing is also proposed. These findings should pave the way for modifying the existing component-based standards for architectural glazing to take the specific features of BIPV glazing into account.

14 SOLAR ENERGY↗
DOE OSTI2020

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. Accordingly, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Additionally, case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Selective Ligand Modification of Cobalt Porphyrins for Carbon Dioxide Electrolysis: Generation of a Renewable H2/CO Feedstock for Downstream Catalytic Hydrogenation

Catalytic hydrogenation is an attractive approach to produce green fuels and chemicals. The building blocks for these processes may be effectively produced from renewable power via direct electrochemical reduction of carbon dioxide in an aqueous media. For the first time, the impact of increasing the local proton concentration of cobalt porphyrin was examined by synthesizing new cobalt porphyrins 2, Co(o-OCH3)TPP and cobalt porphyrin 3, Co(o-OH)TPP. Cobalt porphyrins coated on carbon paper converted carbon dioxide and water into a mixture of hydrogen and carbon monoxide in an aqueous electrolyte at near neutral pH. Increasing the local proton availability of the commercial cobalt porphyrin 1, accelerates hydrogen generation under heterogeneous conditions across the range of potentials tested (-0.85 to -1.5 V vs. Ag/AgCl) and demonstrates high Faradaic efficiencies (ca. 90%) at low over-potentials (ca. 540 mV). The culmination of this work can help identify key parameters that facilitate generation of sustainable reagents for catalytic hydrogenation under practical and scalable conditions.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2023

Data-Driven Modeling of a High Capacity Cryogenic System for Control Optimization

The Cryogenic Moderator System (CMS) is responsible for maintaining a steady flow of cold neutrons for numerous physics experiments at the Spallation Neutron Source (SNS) in Oak Ridge National Laboratory (ORNL). Sudden losses in beam power, known as beam trips, cause a major disturbance to the CMS due to large step changes in cooling demands. Ongoing efforts on upgrading the neutron beam power from 1.4 to 2.0MW are expected to generate larger transients that can further strain the CMS subsystems if they are not properly controlled. To manage such disturbances, four flow valves and one electric heater are adjusted by five decentralized proportional-integral-derivative (PID) controllers. However, the original PID gains were calibrated empirically based only on tracking performance and not based on disturbance rejection. To address this issue without compromising current CMS operations, a control-oriented model was developed to recalibrate the PID controllers offline. The zero-dimensional (0-D) model was based on simple physics-based principles and data-driven system identification techniques. The CMS was broken into several subsystems for analysis, each of which corresponds to a parametric model tied to the thermodynamic states of the working fluid. The model parameters were identified using the nonlinear least squares method where the residuals were calculated from available sensor data. Simulation results show that the proposed model can capture the dynamics of the CMS at steady state and during beam trips.

Maldonado Puente, Bryan↗
DOE OSTI2024

Comparative Evaluation of Control-Oriented Heavy Duty Vehicle Air Drag Coefficient Models

Heavy-duty vehicles (HDVs) are a significant source of fuel consumption and greenhouse gas emissions, prompting solutions such as HDV platooning to mitigate these negative impacts through air drag reduction. The intervehicle distance in an HDV platoon needs to be carefully selected, such that the platoon-level energy efficiency and safety considerations can be well balanced. Underlying this problem lies in accurately modeling the relationship between HDV air drag coefficient and intervehicle distance. Through comprehensive evaluation and comparison, we analyze five control-oriented HDV air drag coefficient models, including the polynomial model, rational polynomial model, rational model, semi-quadratic model, and ridge model. Leveraging Scipy Curve-Fit toolbox and our previously compiled air drag coefficient datasets, we optimally identify the parameters inside each model. The calibrated models are then thoroughly evaluated via five complementary metrics. The comparison results reveal that the semi-quadratic model has the highest overall performance, while the widely adopted rational model only exhibits suboptimal performance.

Best, Micah↗
DOE OSTI2018

Origin of the sensitivity in modeling the glide behaviour of dislocations

The sensitivity in predicting glide behaviour of dislocations has been a long-standing problem in the framework of the Peierls-Nabarro model. The predictions of both the model itself and the analytic formulas based on it are too sensitive to the input parameters. In order to reveal the origin of this important problem in materials science, a new empirical-parameter-free formulation is proposed in the same framework. Unlike previous formulations, it includes only a limited small set of parameters all of which can be determined by convergence tests. Under special conditions the new formulation is reduced to its classic counterpart. In the light of this formulation, new relationships between Peierls stresses and the input parameters are identified, where the sensitivity is greatly reduced or even removed.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Integrating Epidermal Growth Factor Receptor Assay With Clinical Parameters Improves Risk Classification for Relapse and Survival in Head-and-Neck Squamous Cell Carcinoma

Purpose: Epidermal growth factor receptor (EGFR) overexpression has been consistently found to be an independent predictor of local-regional relapse (LRR) after radiotherapy. We assessed the extent by which it can refine risk classification for overall survival (OS) and LRR in patients with head-and-neck squamous cell carcinoma (HNSCC). Methods and Materials: EGFR expression in locally advanced HNSCC was measured by immunohistochemistry in a series of patients randomized to receive accelerated or conventional radiation regimens in a Phase III trial. Subsequently, data of the two series were pooled (N = 533) for conducting a recursive partitioning analysis that incorporated clinical parameters (e.g., performance status, primary site, T and N categories) and four molecular markers (EGFR, p53, Ki-67, and microvessel density). Results: This study confirmed that patients with higher than median levels of tumor EGFR expression had a lower OS (relative risk [RR]: 1.90, p = 0.0010) and a higher LRR (RR: 1.91, p = 0.0163). Of the four markers analyzed, only EGFR was found to contribute to refining classification of patients into three risk classes with distinct OS and LRR outcomes. The addition of EGFR to three clinical parameters could identify patients having up to a fivefold difference in the risk of LRR. Conclusions: Adding pretreatment EGFR expression data to known robust clinical prognostic variables improved the estimation of the probability for OS and LRR after radiotherapy. Its use for stratifying or selecting patients with defined tumor feature and pattern of relapse for enrollment into clinical trials testing specific therapeutic strategy warrants further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Normal Tissue Complication Probability Modeling of Acute Hematologic Toxicity in Patients Treated With Intensity-Modulated Radiation Therapy for Squamous Cell Carcinoma of the Anal Canal

Purpose: To identify dosimetric parameters that correlate with acute hematologic toxicity (HT) in patients with squamous cell carcinoma of the anal canal treated with definitive chemoradiotherapy (CRT). Methods and Materials: We analyzed 33 patients receiving CRT. Pelvic bone (PBM) was contoured for each patient and divided into subsites: ilium, lower pelvis (LP), and lumbosacral spine (LSS). The volume of each region receiving at least 5, 10, 15, 20, 30, and 40 Gy was calculated. Endpoints included grade {>=}3 HT (HT3+) and hematologic event (HE), defined as any grade {>=}2 HT with a modification in chemotherapy dose. Normal tissue complication probability (NTCP) was evaluated with the Lyman-Kutcher-Burman (LKB) model. Logistic regression was used to test associations between HT and dosimetric/clinical parameters. Results: Nine patients experienced HT3+ and 15 patients experienced HE. Constrained optimization of the LKB model for HT3+ yielded the parameters m = 0.175, n = 1, and TD{sub 50} = 32 Gy. With this model, mean PBM doses of 25 Gy, 27.5 Gy, and 31 Gy result in a 10%, 20%, and 40% risk of HT3+, respectively. Compared with patients with mean PBM dose of <30 Gy, patients with mean PBM dose {>=}30 Gy had a 14-fold increase in the odds of developing HT3+ (p = 0.005). Several low-dose radiation parameters (i.e., PBM-V10) were associated with the development of HT3+ and HE. No association was found with the ilium, LP, or clinical factors. Conclusions: LKB modeling confirms the expectation that PBM acts like a parallel organ, implying that the mean dose to the organ is a useful predictor for toxicity. Low-dose radiation to the PBM was also associated with clinically significant HT. Keeping the mean PBM dose <22.5 Gy and <25 Gy is associated with a 5% and 10% risk of HT, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Green micromachining of ceramics using tungsten carbide micro-endmills

This paper presents an experimental analysis on micromachining of green-state silicon carbide (SiC) and aluminum nitride (AlN) using uncoated tungsten carbide (WC) micro-endmills. Although the unique properties of ceramics make them ideal materials for many applications involving micro-scale features, the industrial adaptation of ceramics at the micro-scale has been hindered due to the lack of viable ceramic micro-manufacturing techniques. Green micromachining (GMM), where micro-scale features are created by micromachining green-state ceramics that contain ceramic particles and a polymer binder, offers a potential solution for this problem by affording tremendous improvements in micromachinability with respect to sintered ceramics. After the completion of GMM, the green parts are debound and sintered to obtain the final component with the desired microscale features. In this work, we evaluated GMM characteristics of powder-injection molded green-state SiC and AlN to correlate process conditions and green-material compositions with the micromachining forces, quality, and tool wear. An experimental study that involves full immersion micromilling tests with 254 μm diameter micro-endmills at different cutting speeds and feed rates is conducted. For each of the two materials, two binder states (with and without wax) and two powder states (with micro particles only, or with both micro- and nanoparticles) are investigated. Green micromachining forces, specific energies and the resulting surface roughness are analyzed quantitatively. A qualitative analysis of burr formation and channel quality, as well as a preliminary study on micro-tool wear are also performed. Both the machining conditions and the material compositions are seen to have a strong effect on micro-machinability of green-state ceramics. Overall, we conclude that GMM can be a viable ceramic micro-manufacturing strategy if favorable (or optimal) micromachining and material parameters are identified through the presented (or a similar) micromachinability study.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Metallographic screening of grain boundary engineered type 304 austenitic stainless steel

An electrochemical etching method for the identification of grain boundary engineered type 304 austenitic stainless steel microstructures is described. The method can be applied for rapid microstructure screening to complement electron backscatter diffraction analysis. A threshold parameter to identify grain boundary engineered microstructure is proposed, and the application of metallographic etching for characterising the degree of grain boundary engineering discussed. - Highlights: • As-received (annealed) and grain boundary engineered microstructures were compared. • Electro-chemical polarisation in nitric acid solutions was carried out. • A metallographic screening method has been developed. • The screening method complements EBSD analysis for microstructure identification.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhanced early galaxy formation in JWST from axion dark matter?

We demonstrate that enhanced early galaxy formation can generically arise in axion-like particle (ALP) dark matter (DM) models with a delayed onset of axion field oscillation. In these models, the formation of localized massive objects enhances structure formation, potentially addressing the excess recently observed by the James Webb Space Telescope (JWST), while remaining consistent with existing constraints. We identify viable parameter space with the ALP mass in the range of 10 −22 eV m a < 10 –19 eV. In addition, we show that the ALP parameter regions of interest can lead to intriguing complementary signatures in the small scale structure of DM halos and existing experimental searches for ALPs.

Bird, Simeon↗
DOE OSTI2019

Numerical study of the ignition behavior of a post-discharge kernel in a turbulent stratified crossflow

Ensuring robust ignition is critical for the operability of aeronautical gas-turbine combustors. For ignition to be successful, an important aspect is the ability of the hot gas generated by the spark discharge to initiate combustion reactions, leading to the formation of a self-sustained ignition kernel. This study focuses on this phenomena by performing simulations of kernel ignition in a crossflow configuration that was characterized experimentally. First, inert simulations are performed to identify numerical parameters correctly reproducing the kernel ejection from the ignition cavity, which is here modeled as a pulsed jet. In particular, the kernel diameter and the transit time of the kernel to the reacting mixture are matched with measurements. Considering stochastic perturbations of the ejection velocity of the ignition kernel, the variability of the kernel transit time is also reproduced by the simulations. Subsequently, simulations of a series of ignition sequences are performed with varying equivalence ratio of the fuel-air mixture in the crossflow. The numerical results are shown to reproduce the ignition failure that occurs for the leanest equivalence ratio ($φ = 0.6$). For higher equivalence ratios, the simulations are shown to capture the sensitivity of the ignition to the equivalence ratio, and the kernel successfully transitions into a propagating flame. Significant stochastic dispersion of the ignition strength is observed, which relates to the variability of the transit time of the kernel to the re- active mixture. An analysis of the structure of the ignition kernel also highlights the transition towards a self-propagating flame for successful ignition conditions.

42 ENGINEERING↗
DOE OSTI2018

A calibration method for overconstrained spatial translational parallel manipulators

A calibration method for overconstrained spatial translational parallel mechanisms is proposed. The calibration of a Tri-pyramid overconstrained parallel robot is used as a vehicle to demonstrate the methodology and verify its feasibility. The proposed methodology consists of transforming the overconstrained mechanism of the robot into a non-overconstrained mechanism by replacing its redundant constraints with generalized forces (couples). The kinematic model of the non-overconstrained mechanism is derived using vector loops while its mechanical model is established by the energy method. An integrated kinematic model of the original overconstrained parallel robot is subsequently established by expressing the actual position of the end effector as the sum of the position derived by the kinematic model of the non-overconstrained mechanism and of the force-induced deformations at the end effector derived from the mechanical model. To assess the feasibility and the attainable improvements in accuracy through calibration, the positions of the end effector of the Tri-pyramid robot were measured by a method that uses a Double Ball Bar (DBB) and a 3-axes linear stage with micrometers. Based on the integrated kinematic model of the robot and the measured data, its structural parameters were identified by the Newton–Raphson iteration and least squares methods to complete the calibration. The calibration result shows that accuracy improvements on the order of 90% are achievable.

42 ENGINEERING↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗