Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model based development”

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.

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

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Ion-Exchange Interdiffusion Model with Potential Application to Long-Term Nuclear Waste Glass Performance

Ion exchange is an integral mechanism influencing the corrosion of glasses. Due to the formation of alteration layers in aqueous conditions, it is difficult to conclusively deconvolute the process of ion exchange from other processes, principally dissolution of the glass matrix. Therefore, we have developed a method to isolate alkali diffusion that involves contacting glass coupons with a solution of 6 LiCl dissolved in functionally inert dimethyl sulfoxide. Here, we employ the method at temperatures ranging from 25 to 150 °C with various glass formulations. Glass compositions include simulant nuclear waste glasses, such as SON68 and the international simple glass (ISG), glasses in which the nature of the alkali element was varied, and glasses that contained more than one alkali element. An interdiffusion model based on Fick’s second law was developed and applied to all experiments to extract diffusion coefficients. The model expands established models of interdiffusion to the case where multiple types of alkali sites are present in the glass. Activation energies for alkali ion exchange were calculated and the results are in agreement with those obtained in glass strengthening experiments but are nearly five times higher than values reported for diffusion-controlled processes in nuclear waste glass corrosion experiments. A discussion of the root causes for this apparent discrepancy is provided. The interdiffusion model derived from laboratory experiments is expected to be useful for modeling glass corrosion in a geological repository when the silicon concentration is high.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Learning Interactions in Complex Biological Systems

In this project, we developed novel methods for integrating simulation and data-driven methods to accelerate biomedical discovery. In particular, we exploit agent-based models of disease processes and deep reinforcement learning to identify potential novel multi-drug, patient-specific, and adaptive therapeutic strategies. We use the Innate Immune Response agent-based model (IIRABM) to model sepsis as a demonstration of our approach. We first calibrate the agent-based model using novel stochastic optimization algorithms develop in this project, then use the calibrated model to identify optimal cytokine mediation strategies using deep reinforcement learning (DRL). The learned policy achieves a dramatic reduction in mortality over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are three-fold: 1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, 2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and 3) optimized treatments respond to a patient’s individual disease progression over time, therefore capturing both differences across patients and the inherent randomness of disease progression within a single patient. We hope that this work motivates both considering adaptive, personalized multi-cytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Risk assessment of safety-critical data communication in digital safety feature control system - 205

As one of the safety-critical systems in Advanced Power Reactor-1400 (APR-1400) nuclear power plant (NPP), the Engineered Safety Feature-Component Control System (ESF-CCS) employs safety data link and network communication for transmitting safety component actuation to effectively facilitate various safety-critical field controllers. However, data communication failure risk in the ESF-CCS has yet to be fully quantified, the probability that a safety-critical system in digitalized NPP becomes unsafe due to a network failure must be evaluated to quantify the risk of digital I and C system. In this study, a fault tree model was developed to assess the data link and data network failure-induced unavailability of a safety-critical system function used to generate an automated control signal for actuating design basis accident mitigation equipment. Especially, the risk of data communication failure in a digital safety feature control system was analyzed in consideration of interconnection between safety-critical controllers and the fault-tolerant algorithm implemented in the network configuration. Based on the developed fault tree model, case studies were performed to quantitatively assess the unavailability of ESF-CCS signal generation due to safety data link and network failure and their risk effect on safety signal generation unavailability. This study is expected to provide risk information on the safety-critical data communication in a digitalized NPP instrumentation and control system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

U(VI) bioreduction with emulsified vegetable oil as the electron donor-Model application to a field test

A one-time 2-hour emulsified vegetable oil (EVO) injection in a fast flowing aquifer decreased U discharge to a stream for over a year. Using a comprehensive biogeochemical model developed in the companion article based on microcosm tests, we approximately matched the observed acetate, nitrate, Fe, U, and sulfate concentrations, and described the major evolution trends of multiple microbial functional groups in the field test. While the lab-determined parameters were generally applicable in the field-scale simulation, the EVO hydrolysis rate constant was estimated to be an order of magnitude greater in the field than in the microcosms. The model predicted substantial biomass (sulfate reducers) and U(IV) accumulation near the injection wells and along the side boundaries of the treatment zone where electron donors (long-chain fatty acids) from the injection wells met electron acceptors (sulfate) from the surrounding environment. While EVO retention and hydrolysis characteristics were expected to control treatment longevity, modeling results indicated that electron acceptors such as sulfate may not only compete for electrons but also play a conducive role in degrading complex substrates and enhancing U(VI) reduction and immobilization. As a result, the spacing of the injection wells could be optimized for effective sustainable bioremediation.

Tang, Guoping↗
DOE OSTI2014

Hepatocyte-based in vitro model for assessment of drug-induced cholestasis

Early detection of drug-induced cholestasis remains a challenge during drug development. We have developed and validated a biorelevant sandwich-cultured hepatocytes- (SCH) based model that can identify compounds causing cholestasis by altering bile acid disposition. Human and rat SCH were exposed (24–48 h) to known cholestatic and/or hepatotoxic compounds, in the presence or in the absence of a concentrated mixture of bile acids (BAs). Urea assay was used to assess (compromised) hepatocyte functionality at the end of the incubations. The cholestatic potential of the compounds was expressed by calculating a drug-induced cholestasis index (DICI), reflecting the relative residual urea formation by hepatocytes co-incubated with BAs and test compound as compared to hepatocytes treated with test compound alone. Compounds with clinical reports of cholestasis, including cyclosporin A, troglitazone, chlorpromazine, bosentan, ticlopidine, ritonavir, and midecamycin showed enhanced toxicity in the presence of BAs (DICI ≤ 0.8) for at least one of the tested concentrations. In contrast, the in vitro toxicity of compounds causing hepatotoxicity by other mechanisms (including diclofenac, valproic acid, amiodarone and acetaminophen), remained unchanged in the presence of BAs. A safety margin (SM) for drug-induced cholestasis was calculated as the ratio of lowest in vitro concentration for which was DICI ≤ 0.8, to the reported mean peak therapeutic plasma concentration. SM values obtained in human SCH correlated well with reported % incidence of clinical drug-induced cholestasis, while no correlation was observed in rat SCH. This in vitro model enables early identification of drug candidates causing cholestasis by disturbed BA handling. - Highlights: • Novel in vitro assay to detect drug-induced cholestasis • Rat and human sandwich-cultured hepatocytes (SCH) as in vitro models • Cholestatic compounds sensitize SCH to toxic effects of accumulating bile acids • Drug-induced cholestasis index (DICI) as measure of a drug's cholestatic signature • In vitro findings correlate well with clinical reports on cholestasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

MCNP6 enhancements of delayed-particle production

Over the last decade, there has been an increased interest in the production of delayed-particle signatures from neutron and photon interactions with matter. To address this interest, various radiation transport codes have developed a wide range of delayed-particle physics packages. With the recent merger of the Monte Carlo transport codes MCNP5 and MCNPX, MCNP6 inherited the comprehensive model-based delayed-particle production capabilities developed in MCNPX over the last few years. An integral part of this capability consists of the depletion code CINDER90 which was incorporated into MCNPX in 2004. During this last year, significant improvements have been made to the MCNP6 physics and algorithms associated with delayed-particle production, including the development of a delayed-beta capability, an algorithm enhancement for the delayed-neutron treatment, and a database enhancement for delayed-gamma emission. The delayed-beta feature represents an important component in modeling background signals produced by active interrogation sources. Combined, these improvements provide MCNP6 with a flexible state-of-the-art physics package for generating high-fidelity signatures from fission and activation. This paper provides details of these enhancements and presents results for a variety of fission and activation examples. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2023

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin [Fermilab] (ORCID:0000000157000288↗
DOE OSTI2021

Development of Martini 2.2 parameters for N -glycans: a case study of the HIV-1 Env glycoprotein dynamics

N-linked glycans are ubiquitous in nature and play key roles in biology. For example, glycosylation of pathogenic proteins is a common immune evasive mechanism, hampering the development of successful vaccines. Due to their chemical variability and complex dynamics, an accurate molecular understanding of glycans is still limited by the lack of effective resolution of current experimental approaches. Here, we have developed and implemented a reductive model based on the popular Martini 2.2 coarse-grained force field for the computational study of N-glycosylation. We used the HIV-1 Env as a direct applied example of a highly glycosylated protein. Finally, our results indicate that the model not only reproduces many observables in very good agreement with a fully atomistic force field but also can be extended to study large amount of glycosylation variants, a fundamental property that can aid in the development of drugs and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Modeling and Energy Analysis of Refrigeration Systems in Supermarkets: A Case Study

Refrigeration systems are the single largest consumer of electricity in supermarkets, accounting for approximately 40–60% of total store electricity use. As supermarkets face increasing pressures to improve efficiency and reduce operational costs, understanding the energy performance of refrigeration systems in supermarkets has become essential. In this paper, we investigate the energy performance and patterns of the refrigeration system using field measurements in one supermarket. The compressor coefficient of performance (COP) averaged 4.5 during nighttime steady-state operation, providing a practical efficiency benchmark. We develop simple physical-based load models for the display cases in the supermarket, where the refrigeration system uses R404A. The results show the display case models achieve a good agreement with measured data, validating its utility for energy estimation during unoccupied hours. Finally, the analysis reveals several challenges of data collection systems for refrigeration systems in the supermarket industry as well.

Chen, Yimin [ORNL] (ORCID:0000000338181655)↗
DOE OSTI2019

Characterization of CO 2 storage and enhanced oil recovery in residual oil zones

Residual oil zones (ROZs) are reservoirs in which oil is swept over geologic time period and exists at residual saturation. The oil in such reservoirs cannot be commercially exploited using conventional oil recovery methods as the oil exists at residual oil saturation. Instead, enhanced oil recovery methods such as CO 2 injection are required. Recently, ROZs have been increasingly studied as potential CO 2 storage targets. In spite of increased interest in ROZs, there are significant gaps in the knowledge of parameters and processes that impact CO 2 storage and oil recovery. In this work, we identify key geologic and operational characteristics that affect CO 2 storage capacity and oil recovery potential by performing Monte Carlo simulations and sensitivity analysis. In addition to CO 2 storage capacity, we also characterize the long-term CO 2 fate in ROZs. The distinction of CO 2 storage in ROZs from conventional oil reservoirs and saline aquifers are also characterized. Furthermore, predictive models based on machine learning techniques are developed to estimate CO 2 storage and oil production potentials for ROZs. The applicability of the predictive models is demonstrated for five ROZs in the Permian Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

TU-FG-209-09: Mathematical Estimation and Experimental Measurement of Patient Free-In-Air Skin Entrance Exposure During a Panoramic Dental X-Ray Procedure

Purpose: To develop a simple mathematical model for estimating the patient free-in-air skin entrance exposure (SEE) during a panoramic dental x-ray that does not require the use of a head phantom. This eliminates issues associated with phantom centering and the mounting of a detector on the phantom for routine QC testing. Methods: We used a Sirona Orthophos XG panoramic radiographic unit and a Radcal Accu-Gold system for this study. A solid state detector was attached over the slit of the Orthophos’ sensor with the help of a custom-built jig. A single measurement of the free-in-air exposure at this position was taken over a full panoramic scan. A mathematical model for estimating the SEE was developed based upon this measurement, the system geometry, x-ray field beam width, and x-ray sweep angle. To validate the model, patient geometry was simulated by a 16 cm diameter PMMA CTDI phantom centered at the machine’s isocenter. Measurements taken on the phantom’s surface were made using a solid state detector with lead backing, an ion chamber, and the ion chamber with the phantom wrapped in lead to mitigate backscatter. Measurements were taken near the start position of the tube and at 90 degrees from the start position. Results: Using the solid state detector, the average SEE was 23.5+/−0.02 mR and 55.5+/−0.08 mR at 64 kVp and 73 kVp, respectively. With the lead-wrapping, the measurements from the ion chamber matched those of the solid state detector to within 0.1%. Preliminary results gave the difference between the mathematical model and the phantom measurements to be approximately 5% at both kVps. Conclusion: Reasonable estimates of patient SEE for panoramic dental radiography can be made using a simple mathematical model without the need for a head phantom.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system. Results We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements. Conclusions Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

TRANSP-based closed-loop simulations of current profile optimal regulation in NSTX-Upgrade

Active control of the toroidal current density profile is critical for the upgraded National Spherical Torus eXperiment device (NSTX-U) to maintain operation at the desired high-performance, MHD-stable, plasma regime. Initial efforts towards current density profile control have led to the development of a control-oriented, physics-based, plasma-response model, which combines the magnetic diffusion equation with empirical correlations for the kinetic profiles and the non-inductive current sources. The developed control-oriented model has been successfully tailored to the NSTX-U geometry and actuators. Moreover, a series of efforts have been made towards the design of model-based controllers, including a linear-quadratic-integral optimal control strategy that can regulate the current density profile around a prescribed target profile while rejecting disturbances. In this work, the tracking performance of the proposed current-profile optimal controller is tested in numerical simulations based on the physics-oriented code TRANSP. Furthermore, these high-fidelity closed-loop simulations, which are a critical step before experimental implementation and testing, are enabled by a flexible framework recently developed to perform feedback control design and simulation in TRANSP.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2010

Analysis of char-slag interaction and near-wall particle segregation in entrained-flow gasification of coal

The fate of carbon particles during entrained-flow gasification of coal in the slagging regime is analyzed. More specifically, the study addresses the relevance of segregation of carbon particles in a near-wall region of the gasifier to coal conversion. Segregation of carbon particles is analyzed considering the effects of turbulence- and swirl-promoted particle migration toward the wall, interaction of the impinging particles with the wall ash layer, coverage of the slag layer by refractory carbon particles, accumulation of carbon particles in a dense-dispersed phase near the wall of the gasifier. Operating conditions of the gasifier and slag properties may be combined so as to give rise to a variety of conversion regimes characterized by distinctively different patterns of carbon particles segregation. A simple 1D model of an entrained-flow gasifier has been developed based on the conceptual framework of carbon particle segregation. The model aims at providing a general assessment of the impact of the different patterns of carbon particle segregation on the course and extent of carbon gasification. A sensitivity analysis with reference to selected model parameters is performed to identify key processes controlling carbon segregation and their impact on the gasifier performance. (author)

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2010

Temperature Dependent Constitutive Modeling for Magnesium Alloy Sheet

Magnesium alloys have been increasingly used in automotive and electronic industries because of their excellent strength to weight ratio and EMI shielding properties. However, magnesium alloys have low formability at room temperature due to their unique mechanical behavior (twinning and untwining), prompting for forming at an elevated temperature. In this study, a temperature dependent constitutive model for magnesium alloy (AZ31B) sheet is developed. A hardening law based on non linear kinematic hardening model is used to consider Bauschinger effect properly. Material parameters are determined from a series of uni-axial cyclic experiments (T-C-T or C-T-C) with the temperature ranging 150-250 deg. C. The influence of temperature on the constitutive equation is introduced by the material parameters assumed to be functions of temperature. Fitting process of the assumed model to measured data is presented and the results are compared.

36 MATERIALS SCIENCE↗