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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Tumor response parameters for head and neck cancer derived from tumor-volume variation during radiation therapy

Purpose: The main goal of this paper is to reconstruct a distribution of cell survival fractions from tumor-volume variation for a heterogeneous group of head and neck cancer patients and compare this distribution to the data from predictive assays. Methods: To characterize the tumor-volume variation during radiation therapy treatment, the authors use a two-level tumor-volume model of cell population that separates the entire tumor cell population into two subpopulations of viable cells and lethally damaged cells. This parameterized radiobiological model is integrated with a least squares objective function and a simulated annealing optimization algorithm to describe time-dependent tumor-volume variation rates in individual patients. Several constraints have been used in the optimization problem because tumor-volume variation during radiotherapy is described by a sum of exponentials; therefore, the problem of accurately fitting a model to measured data is ill-posed. The model was applied to measured tumor-volume variation curves from a clinical study on tumor-volume variation during radiotherapy for 14 head and neck cancer patients in which an integrated CT/linear particle accelerator (LINAC) system was used for tumor-volume measurements. Results: The two-level cell population tumor-volume modeling is capable of describing tumor-volume variation throughout the entire treatment for 11 of the 14 patients. For three patients, the tumor-volume variation was described only during the initial part of treatment, a fact that may be related to the neglected hypoxia in the two-level approximation. The predicted probability density distribution for the survival fractions agrees with the data obtained using in vitro studies with predictive assays. The mean value 0.35 of survival fraction obtained in this study is larger than the value 0.32 from in vitro studies, which could be expected because of greater repair in vivo. The mean half-life obtained in this study for the head-and-neck squamous cell carcinoma (SCC) is equal to 3.8 mean potential doubling times, which agrees with 4.0 mean potential doubling times obtained previously for lung SCC. Conclusions: The distribution of cell survival fractions obtained in this study support the hypothesis that the tumor-volume variation during radiotherapy treatment for head and neck cancer can be described by the two-level cell population tumor-volume model. This model can be used for in vivo evaluation of patient-specific radiobiological parameters that are needed for tumor-control probability evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

ExaSGD: 2021 Kernel Thrust Activities

The Kernel Thrust milestone ADSE22-214 covers the development of device-capable optimization algorithms and solvers technologies required by the ExaSGD project’s software stack in order to solve security-constrained alternating current optimal power flow (SC-ACOPF) problems on emerging exascale architectures. To this extent, in FY21 the main objective of the Kernel Thrust was (i) provide robust optimization solver(s) that run efficiently on hardware accelerator devices (i.e., NVIDIA and AMD GPUs) to perform intra-node computations and (ii) provide coarse-grain parallel optimization capabilities that exploit the decomposition opportunities present in the SC-ACOPF challenge problems to provide exascale-capable solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A computational fluid dynamics simulation framework for ventricular catheter design optimization

OBJECTIVE Cerebrospinal fluid (CSF) shunts are the primary treatment for patients suffering from hydrocephalus. While proven effective in symptom relief, these shunt systems are plagued by high failure rates and often require repeated revision surgeries to replace malfunctioning components. One of the leading causes of CSF shunt failure is obstruction of the ventricular catheter by aggregations of cells, proteins, blood clots, or fronds of choroid plexus that occlude the catheter’s small inlet holes or even the full internal catheter lumen. Such obstructions can disrupt CSF diversion out of the ventricular system or impede it entirely. Previous studies have suggested that altering the catheter’s fluid dynamics may help to reduce the likelihood of complete ventricular catheter failure caused by obstruction. However, systematic correlation between a ventricular catheter’s design parameters and its performance, specifically its likelihood to become occluded, still remains unknown. Therefore, an automated, open-source computational fluid dynamics (CFD) simulation framework was developed for use in the medical community to determine optimized ventricular catheter designs and to rapidly explore parameter influence for a given flow objective. METHODS The computational framework was developed by coupling a 3D CFD solver and an iterative optimization algorithm and was implemented in a high-performance computing environment. The capabilities of the framework were demonstrated by computing an optimized ventricular catheter design that provides uniform flow rates through the catheter’s inlet holes, a common design objective in the literature. The baseline computational model was validated using 3D nuclear imaging to provide flow velocities at the inlet holes and through the catheter. RESULTS The optimized catheter design achieved through use of the automated simulation framework improved significantly on previous attempts to reach a uniform inlet flow rate distribution using the standard catheter hole configuration as a baseline. While the standard ventricular catheter design featuring uniform inlet hole diameters and hole spacing has a standard deviation of 14.27% for the inlet flow rates, the optimized design has a standard deviation of 0.30%. CONCLUSIONS This customizable framework, paired with high-performance computing, provides a rapid method of design testing to solve complex flow problems. While a relatively simplified ventricular catheter model was used to demonstrate the framework, the computational approach is applicable to any baseline catheter model, and it is easily adapted to optimize catheters for the unique needs of different patients as well as for other fluid-based medical devices.

Neurosciences & Neurology↗
DOE OSTI2013

Determination of Optimal Operating Schemes for a Multi-Reservoir System Under Environmental Constraints

In strati ed reservoirs, both dam tailwater discharge and thermal plant intake water quality and temperature can be highly dependent on structure depth. A two- dimensional laterally-averaged model allows for better prediction of water quality over time at specfi c depths. Because high- fidelity models are typically too computationally expensive for direct inclusion within optimization algorithms, water quality is incorporated using one dimensional models are simple flow requirements. Water quality predictions can be incorporated within the optimization process through using surrogate modeling methods, in this application artificial neural network (ANN) models. ANNs are flexible machine learning tools for function approximation composed of a structure of neurons assembled within a multi-layer architecture. They are capable of handling large amounts of training data and modeling nonlinear dynamic systems, making ANNs a well-suited method for this application. This report illustrates the development of ANN models to emulate the hydrodynamic and water quality modeling capabilities of the high- fidelity, two-dimensional CE-QUAL-W2 (W2) model, as well as a linked riverine reservoir system optimization process which accounts for energy generation, water balance and hydraulics, and compliance point water quality. A process for hourly hydropower generation planning is demonstrated on a pair of reservoirs linked in series. The two reservoirs are U.S. Army Corps of Engineers projects with hydropower capabilities on the Cumberland River near Nashville, Tennessee, USA.

13 HYDRO ENERGY↗
DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗
DOE OSTI2018

Developing Fugitive Emissions Sensor Networks

This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Community Resilience Through Rapid Restoration Leveraging Distributed Energy Resources (DERs) and Low-Cost Sensors

Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

The Computer Program LIAR for Beam Dynamics Calculations in Linear Accelerators

Linear accelerators are the central components of the proposed next generation of linear colliders. They need to provide acceleration of up to 750 GeV per beam while maintaining very small normalized emittances. Standard simulation programs, mainly developed for storage rings, do not meet the specific requirements for high energy linear accelerators. We present a new program LIAR ('LInear Accelerator Research code') that includes wakefield effects, a 6D coupled beam description, specific optimization algorithms and other advanced features. Its modular structure allows to use and to extend it easily for different purposes. The program is available for UNIX workstations and Windows PC's. It can be applied to a broad range of accelerators. We present examples of simulations for SLC and NLC.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs

We develop an adaptive, multivariate partitioning algorithm for solving nonconvex, Mixed-Integer Nonlinear Programs (MINLPs) with polynomial functions to global optimality. In particular, we present an iterative algorithm that exploits piecewise, convex relaxation approaches via disjunctive formulations to solve MINLPs that is different than conventional spatial branch-and-bound approaches. The algorithm partitions the domains of variables in an adaptive and non-uniform manner at every iteration to focus on productive areas of the search space. Furthermore, domain reduction techniques based on sequential, optimization-based bound-tightening and piecewise relaxation techniques, as a part of a presolve step, are integrated into the main algorithm. Finally, we demonstrate the effectiveness of the algorithm on well-known benchmark problems (including Pooling and Blending instances) from MINLPLib and compare our algorithm with state-of-the-art global optimization solvers. With our novel approach, we solve several large-scale instances, some of which are not solvable by state-of-the-art solvers. We also succeed in reducing the best known optimality gap for a hard, generalized pooling problem instance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Sequence of polyhedral relaxations for nonlinear univariate functions

Here, given a nonlinear, univariate, bounded, and differentiable function f(x), this article develops a sequence of Mixed Integer Linear Programming (MILP) and Linear Programming (LP) relaxations that converge to the graph of f(x) and its convex hull, respectively. Theoretical convergence of the sequence of relaxations to the graph of the function and its convex hull is established. For nonlinear non-convex optimization problems, the relaxations presented in this article can be used to construct tight MILP and LP relaxations. These MILP and the LP relaxations can also be used with MILP-based and spatial branch-and-bound based global optimization algorithms, respectively.

42 ENGINEERING↗
DOE OSTI2016

Chance-Constrained System of Systems Based Operation of Power Systems

In this paper, a chance-constrained system of systems (SoS) based decision-making approach is presented for stochastic scheduling of power systems encompassing active distribution grids. Based on the concept of SoS, the independent system operator (ISO) and distribution companies (DISCOs) are modeled as self-governing systems. These systems collaborate with each other to run the entire power system in a secure and economic manner. Each self-governing system accounts for its local reserve requirements and line flow constraints with respect to the uncertainties of load and renewable energy resources. A set of chance constraints are formulated to model the interactions between the ISO and DISCOs. The proposed model is solved by using analytical target cascading (ATC) method, a distributed optimization algorithm in which only a limited amount of information is exchanged between collaborative ISO and DISCOs. In this paper, a 6-bus and a modified IEEE 118-bus power systems are studied to show the effectiveness of the proposed algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

TH-C-BRD-12: Robust Intensity Modulated Proton Therapy Plan Can Eliminate Junction Shifts for Craniospinal Irradiation

Purpose: The passive scattering proton therapy (PSPT) technique is the commonly used radiotherapy technique for craniospinal irradiation (CSI). However, PSPT involves many numbers of junction shifts applied over the course of treatment to reduce the cold and hot regions caused by field mismatching. In this work, we introduced a robust planning approach to develop an optimal and clinical efficient techniques for CSI using intensity modulated proton therapy (IMPT) so that junction shifts can essentially be eliminated. Methods: The intra-fractional uncertainty, in which two overlapping fields shift in the opposite directions along the craniospinal axis, are incorporated into the robust optimization algorithm. Treatment plans with junction sizes 3,5,10,15,20,25 cm were designed and compared with the plan designed using the non-robust optimization. Robustness of the plans were evaluated based on dose profiles along the craniospinal axis for the plans applying 3 mm intra-fractional shift. The dose intra-fraction variations (DIV) at the junction are used to evaluate the robustness of the plans. Results: The DIVs are 7.9%, 6.3%, 5.0%, 3.8%, 2.8% and 2.2%, for the robustly optimized plans with junction sizes 3,5,10,15,20,25 cm. The DIV are 10% for the non-robustly optimized plans with junction size 25 cm. The dose profiles along the craniospinal axis exhibit gradual and tapered dose distribution. Using DIVs less than 5% as maximum acceptable intrafractional variation, the overlapping region can be reduced to 10 cm, leading to potential reduced number of the fields. The DIVs are less than 5% for 5 mm intra-fractional shifts with junction size 25 cm, leading to potential no-junction-shift for CSI using IMPT. Conclusion: This work is the first report of the robust optimization on CSI based on IMPT. We demonstrate that robust optimization can lead to much efficient carniospinal irradiation by eliminating the junction shifts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Microhole drilling technology utilizing a golden section search algorithm

A fundamental issue in microhole drilling is that delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes necessary to realize the envisioned cost savings An optimization algorithm called a golden section search (GSS) was used to systematically identify the preferred WOB for a given set of conditions. This research focused on implementing and evaluating two low WOB drilling technologies for microhole drilling: - Laser-assisted mechanical drill, which was tested in the laboratory - Lightly modified commercial off-the-shelf (COTS) percussive hammer, which was tested in a limited field test. Data were collected for microhole GSS using WOB optimization via simulation as well as at the Blue Canyon Dome Site in Socorro, NM. Information on the attached files and folders are as follows: - the .tdms files are LabView data files, which can be opened within Excel using a .tdms add-in or using a Matlab .tdms converter - the .tdms_index files are part of the .tdms file structure - sampling rate, column headers, and length data within the .tdms files follow SOP when utilizing Excel and/or Matlab as described above

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Using a derivative-free optimization method for multiple solutions of inverse transport problems

Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a mesh adaptive direct search (MADS) algorithm is incorporated into the local phase. Furthermore, numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.

42 ENGINEERING↗
DOE OSTI2015

Trajectory Modulated Arc Therapy: A Fully Dynamic Delivery With Synchronized Couch and Gantry Motion Significantly Improves Dosimetric Indices Correlated With Poor Cosmesis in Accelerated Partial Breast Irradiation

Purpose: To develop planning and delivery capabilities for linear accelerator–based nonisocentric trajectory modulated arc therapy (TMAT) and to evaluate the benefit of TMAT for accelerated partial breast irradiation (APBI) with the patient in prone position. Methods and Materials: An optimization algorithm for volumetrically modulated arc therapy (VMAT) was generalized to allow for user-defined nonisocentric TMAT trajectories combining couch rotations and translations. After optimization, XML scripts were automatically generated to program and subsequently deliver the TMAT plans. For 10 breast patients in the prone position, TMAT and 6-field noncoplanar intensity modulated radiation therapy (IMRT) plans were generated under equivalent objectives and constraints. These plans were compared with regard to whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose. Results: For TMAT APBI, nonisocentric collision-free horizontal arcs with large angular span (251.5 ± 7.9°) were optimized and delivered with delivery time of ∼4.5 minutes. Percentage changes of whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose for TMAT relative to IMRT were −10.81% ± 6.91%, −27.81% ± 7.39%, −14.82% ± 9.67%, and 39.40% ± 10.53% (P≤.01). Conclusions: This is a first demonstration of end-to-end planning and delivery implementation of a fully dynamic APBI TMAT. Compared with IMRT, TMAT resulted in marked reduction of the breast tissue volume irradiated at high doses.

62 RADIOLOGY AND NUCLEAR MEDICINE↗