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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50–900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Light Water Reactor Sustainability Program: Use of Time Distributions to Predict Operator Procedure Performance in Dynamic Human Reliability Analysis

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework affords software capable of conducting human reliability analysis (HRA) using a dynamic approach built around operating procedures (OPs) from nuclear power plants (NPPs). Previous HUNTER reports document the development of this software tool, the coupling of HUNTER to the simulator code, the collection of operator performance data by using simulators to calibrate HUNTER models, and linking HUNTER to probabilistic risk assessment (PRA) software. The present report largely addresses two topics. The first is a new function in HUNTER called the HUNTER Procedure Performance Predictor (P3). HUNTER P3 uses HUNTER’s built in Monte Carlo tools featuring human performance variability to identify potential error traps in procedures. The second topic is time distribution analysis to generate time inputs for dynamic HRA. The current analysis was performed to investigate time distributions for task primitives, which are the minimum task unit of analysis used in dynamic HRA modeling. Using the time distribution data, the elapsed time for human actions in an extended loss of AC power (ELAP) scenario is then investigated. Time data and prediction are essential for modeling procedure performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Using Models to Predict the Hygrothermal Performance of Equipment and Piping Insulation

The development of hygrothermal models began in earnest in the 1990s, with the initial emphasis on the performance of building envelope systems. Numerous large-scale building envelope failures associated with water intrusion drove this development. The physical principles of moisture transfer that apply to building envelopes are equally valid for equipment and piping insulation systems. The main advantage of modeling is that carefully characterized models can predict the long-term hygrothermal performance of any system under different climatic and interior conditions. Thermal insulations are commonly used on outdoor equipment and piping for different applications, such as thermal energy conservation and freeze protection. The insulated equipment usually experiences moisture intrusion due to many factors, such as the duration and frequency of moisture exposure, equipment operating temperature, insulation type, exposure temperature, weather conditions, equipment thermal cycling, and weatherproofing condition. Hygrothermal models have been employed to estimate the moisture pickup of insulated equipment and piping systems, as well as the moisture redistribution and subsequent change in thermal performance as these systems become contaminated with moisture. The hygrothermal model provides insights on the parameters influencing the time of wetness, the lateral redistribution of moisture along the pipe away from the leak, and the ease by which water escapes the insulation/jacketing systems. It has been shown that the moisture leak effect decreases rapidly with distance from the leak and that one- and two-dimensional hygrothermal simulations help to identify solutions to reduce conditions that support corrosion under insulation (CUI).

Shrestha, Som↗
DOE OSTI2022

Predicting Solar Cell Performance from Terahertz and Microwave Spectroscopy

Abstract Mobilities and lifetimes of photogenerated charge carriers are core properties of photovoltaic materials and can both be characterized by contactless terahertz or microwave measurements. Here, the expertise from fifteen laboratories is combined to quantitatively model the current‐voltage characteristics of a solar cell from such measurements. To this end, the impact of measurement conditions, alternate interpretations, and experimental inter‐laboratory variations are discussed using a (Cs,FA,MA)Pb(I,Br) 3 halide perovskite thin‐film as a case study. At 1 sun equivalent excitation, neither transport nor recombination is significantly affected by exciton formation or trapping. Terahertz, microwave, and photoluminescence transients for the neat material yield consistent effective lifetimes implying a resistance‐free JV‐curve with a potential power conversion efficiency of 24.6 %. For grainsizes above ≈20 nm, intra‐grain charge transport is characterized by terahertz sum mobilities of ≈32 cm 2 V −1 s −1 . Drift‐diffusion simulations indicate that these intra‐grain mobilities can slightly reduce the fill factor of perovskite solar cells to 0.82, in accordance with the best‐realized devices in the literature. Beyond perovskites, this work can guide a highly predictive characterization of any emerging semiconductor for photovoltaic or photoelectrochemical energy conversion. A best practice for the interpretation of terahertz and microwave measurements on photovoltaic materials is presented.

14 SOLAR ENERGY↗
DOE OSTI2021

Predicted Heat Flux Performance of Actively Cooled Tungsten-Armored Graphitic Foam Monoblocks

Tungsten (W)–armored graphitic foam monoblocks were developed for applications requiring high-Z plasma-facing material in long-pulse fusion experiments and ultimately deuterium-tritium fusion reactors. The monoblocks are an integrated material system combining the advantages of a chemical vapor deposited (CVD) W coating with a high-conductivity graphitic foam. The W is a high-melting-point, high-Z material with low tritium retention. The graphitic foam coupled to a swirl tube serves as a high-thermal-conductivity heat sink that cannot melt, although it can sublime at much higher temperatures than copper melts. Together, they comprise a robust plasma-facing component (PFC) weighing roughly 5% of an all-W component or 17% of a traditional W-coated copper heat sink.A single-channel mock-up consisting of four graphitic foam monoblocks equipped with a water-cooled swirl tube was fabricated for eventual testing in the 60-kW, EB-60, rastered electron beam at the Applied Research Laboratory of The Pennsylvania State University. Two monoblocks have a thin 50-μm-thick coating of pure W chemically vapor deposited over NbC and pure Nb interlayers. Two others have a 2-mm-thick pure W coating CVD on graphitic monoblocks using the same interlayers. The mock-up will be cooled with available 10 m/s, 0.7 MPa water with a 22°C inlet temperature and subjected to varying uniform heat loads up to 20 MW/m 2 . It is equipped with type-K thermocouples at various depths, and calibrated infrared thermography and spot pyrometry will be used to characterize the heated surface. Real-time water calorimetry will be used to ascertain the absorbed steady-state power and infer the heat flux during testing. Since testing cannot be done under prototypic divertor flow conditions, it is necessary to predict the thermal response of this novel PFC system and investigate the power sharing between radiation and convection at divertor heat flux levels and its inherent ability to avoid critical heat flux. Results are reported for predictions obtained from computational fluid dynamics models up to 30 MW/m 2 of steady-state uniform heat flux. Leading-edge heat loads of 30 MW/m 2 on a 2-mm-wide side strip were also investigated to ascertain if coating delamination is likely.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A first principles framework to predict the transient performance of latent heat thermal energy storage

Thermal energy storage (TES) is increasingly recognized as an essential component of efficient Combined Heat and Power (CHP), Concentrated Solar Power (CSP), Heating Ventilation and Air Conditioning (HVAC), and refrigeration as it reduces peak demand while helping to manage intermittent availability of energy (e.g., from solar or wind). Latent Heat Thermal Energy Storage (LHTES) is a viable option because of its high energy storage density. Parametric analysis of LHTES in terms of dimensionless numbers is highly desired as a tool to model LHTES systems. One approach is to develop a model equation so as to minimize the error between the model and data obtained from experiments or simulations. While this approach can produce an accurate correlation applicable within the range of data used for its creation, it does not provide physical understanding of the rate-limiting process controlling the transient behavior of the device. In this paper we present an alternative approach whereby the potential rate-limiting processes are identified from first principles and then the key process is determined as a function of time as a LHTES device is charged. For example, in a simple geometry, the melt-fraction can be expected to vary linearly in time if the heat transfer rate is limited by natural convection of the phase changing material and we show it scales with the PCM Grashof number as $Gr^1_p$ and PCM Prandtl number as $Pr_p^{(1/3)}$. On the other hand, if surface area of solid PCM limits the heat transfer rate, the melt fraction increases asymptotically to reach full melting. The existence of these linear and asymptotic regions and the $Gr^1_pP r^{1/3}_p$ shape of the melt fraction curve is verified using our database of 64 simulations. Of practical importance in designing LHTES devices is the melt fraction at which the heat transfer rate ceases to be limited by convection, after which the heat storage rate deteriorates. For our geometry, this is found to be about 90%. This test case of our methodology shows the value of our approach, that predicting heat storage rate based on the rate-limiting physical phenomenon as a function of time is an effective approach to modeling LHTES devices.

25 ENERGY STORAGE↗
DOE OSTI2020

Predicting Execution Times for Disk-based and In-Situ Parallel Data Analytics (Final Technical Report)

In recent years, there has been a significant amount of interests in in-situ analytics on simulation programs. For a variety of reasons, it is desirable to be able to predict the execution time of an analytics program. At the same time, frameworks such as MapReduce have become popular for scientific data analytics. This paper focuses on developing performance models for predicting execution time of parallel data analytics, with a special emphasis on in-situ analytics. We take two distinct approach towards performance prediction. We first expand SKOPE (a SKeleton framewOrk for Performance Exploration) with performance models for disk data read, cache performance, and page fault penalty. Second, an analytical performance model is also developed. We have evaluated our performance prediction framework as well as the analytical model on three hardware setups with well-known data mining algorithms implemented in three programming paradigms, MapReduce, MATE (a MapReduce-like parallel system with an alternate API for multi-core environments) and Smart (a MapReduce-like framework for in-situ analytics). Results show that our performance prediction framework along with the incorporated performance models are capable of accurately predicting execution times for parallel scientific analytics on different hardware setups.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Relating Solvent Parameters to Electrochemical Properties to Predict the Electrochemical Performance of Vanadium Acetylacetonate for Non-Aqueous Redox Flow Batteries

Non-aqueous redox flow batteries have shown promise for applications in grid energy storage. Increasing the efficiency of these batteries by developing the electrolyte chemistries is needed. Herein, we investigate the correlation between solvent properties and the electrochemical parameters of vanadium acetylacetonate V(acac) 3 . Using cyclic voltammetry (CV) and rotating disk electrode experiments (RDE), we show that trends in the performance of the V(acac) 3 kinetics are directly related to solvent properties. In this study, we found strong relationships between the solvents polarity, viscosity, and donor number with the electrochemical behavior of V(acac) 3 in terms of the electrochemical working widow, electron kinetics and stability towards cycling. Based on these finding, we also demonstrate how solvent selection can be improved with limited a priori knowledge.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of physically transparent, predictive structure-performance relationships for rational design of multi-component catalytic materials. Final Report

Solid surfaces act as catalysts for many important chemical processes, including commodity chemicals production, energy conversion and pollution mitigation. The outcome of catalytic processes is mainly governed by interactions of adsorbates with active sites on the catalyst surface. These interactions can be dramatically affected by the changes in the local characteristics of the active site, which can be accomplished by an introduction of various promoters and poisons, or by alloying with other metal atoms. In fact, many commercial heterogeneous catalysts contain metal nanoparticles promoted by various additives. Our goal is to develop fundamental understanding about the impact of perturbations in the local geometry of a catalytic site on the local surface reactivity. These insights are crucial for a more systematic and rational catalyst design. The project aims to advance the field of catalysis by formulating reliable structure/performance relationships and utilizing these relationships to identify novel catalytic materials. Benefits for DOE and our society are in the development of fundamental theories that guide the process of discovery of energy-efficient and environmentally friendly heterogeneous catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluation of Envelope Energy in a High-Performance Manufactured Home in California

There are important applications for a robust energy model for new construction manufactured homes that can characterize the individual performance of the opaque wall and ceiling components of the building envelope. The efficacy of different insulation materials can be assessed with the confidence that modeled performance will closely predict installed performance. This study uses careful calibration of a standard energy model with a short-term co-heating test in an installed manufactured home to de-aggregate the contributions of planar wall sections from overall building envelope thermal resistance. The methodology takes into account thermal mass effects, solar gains, and physical adjustments to the structure to achieve successful calibration and to inform adjustments to the test procedure for future study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Online System ID for Predicting Power Plant Performance Throughout Cycling Operations

This presentation represents a review of the background research conducted by NETL to apply artificial intelligence, i.e. auto-recursive algorithms and data analytics to detect leaks in utility scale boilers and laboratory power systems. The new project being funded by the Advanced Sensors and Controls Program is part of the Field Work Proposal funded in EY21 as Task 53 to demonstrate the application of these techniques on a utility scale power system.

Shadle, Lawrence↗