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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulations and Experimental Verifications of an Algorithm for Radiation Source Mapping and Navigational Path Generation

Accurate and efficient mapping and localization of both ionizing and non-ionizing radiation sources are important across many different fields. As such, a versatile mapping and navigational path generation algorithm, which can be applied to any point source measurements that follow an inverse-square characteristic, was developed using non-linear least squares methods. Forty thousand simulations were performed on the algorithm, which located sources successfully in a 10 m × 10 m × 10 m three-dimensional space with a success rate of over 80% across different noise functions, given a proportional constant of 10 to 1,000. The algorithm was also verified experimentally with small-scale radioactive decontamination of a 70 cm × 70 cm surface and localization of a lost Wi-Fi router in a 70 m × 70 m open field. One hundred twenty-one measurements were taken from each experiment, which were then fed into the algorithm for navigation. For the radioactive 137Cs source, the estimated locations were within 7 cm × 7 cm of the answer in 79.3% of the scenarios, while the Wi-Fi router was located to within 7 m × 7 m in 57.9% of the tests. In general, the method requires much less information and data than a geographically comprehensive survey and thus shows a lot of potential for practical applications, such as lost source retrieval with unmanned aerial vehicles, small-scale decontamination, mapping undocumented Wi-Fi routers or radio towers, and radiation simulation with radio signals. Here, the different failure modes, desirable features, and potential improvements were also identified but remain as future work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Controlling error orientation to improve quantum algorithm success rates

The success probability of a quantum algorithm constructed from noisy quantum gates cannot be accurately predicted from single-parameter metrics that compare noisy and ideal gates. We illustrate this concept by examining a system with coherent errors and comparing algorithm success rates for different choices of two-qubit gates that are constructed from composite pulse sequences, where the residual gate errors are related by a unitary transformation. As a result, all of the sequences have the same error relative to the ideal gate under any distance measure that is invariant under unitary transformations. However, the circuit success can vary dramatically by choosing error orientations that do not affect the final outcome and error orientations that cancel between conjugate controlled-nots, as demonstrated here with Clifford circuits, compiled Toffoli gates, and quantum simulation algorithms. Furthermore, the results point to the utility of both minimizing the error and optimizing the error direction and also to the advantages of using multiple control sequences for the same gate type within a single algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Projection algorithm for state preparation on quantum computers

We present an efficient method to prepare states of a many-body system on quantum hardware, first isolating individual quantum numbers and then using time evolution to isolate the energy. Our method in its simplest form requires only one additional auxiliary qubit. The total time evolved for an accurate solution is proportional to the ratio of the spectrum range of the trial state to the gap to the lowest excited state, a substantial improvement over other projection algorithms, and the accuracy increases exponentially with the time evolved. Isolating the quantum numbers is efficient because of the known eigenvalues and increases the gap thus shortening the propagation time required. The success rate of the algorithm, or the probability of producing the desired state, is a simple function of measurement times and phases and is dominated by the square overlap of the original state to the desired state. We present examples from the nuclear shell model and the Heisenberg model. In conclusion, we compare this algorithm to previous algorithms for short evolution times and discuss potential further improvements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Noisy intermediate-scale quantum algorithms

A universal fault-tolerant quantum computer that can efficiently solve problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement toward realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e., qubits that are not error corrected, and therefore perform imperfect operations within a limited coherence time. In the search for achieving quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry, and combinatorial optimization. The overarching goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, a thorough summary of NISQ computational paradigms and algorithms is provided. The key structure of these algorithms and their limitations and advantages are discussed. Finally, a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices is additionally provided.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2023

A Hybrid Optimization and Deep Learning Algorithm for Cyber-Resilient DER Control

With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.

cyber-resilient algorithm↗
DOE OSTI2019

Coherency-Based Detection Algorithm for Synchrophasor Cyberattacks

The wide area monitoring system (WAMS) is critical for power system situational awareness, but represents a growing cybersecurity vulnerability. Malicious adversaries may seek to compromise one or more PMUs in order to effect control decisions that unnecessarily disrupt typical grid operations. One example of a particularly pernicious attack vector is the spoofing or replaying of a fault event using one or more compromised PMUs. This work documents the development and validation of a coherency-based cyberattack detection algorithm that integrates a sliding-window singular value decomposition (SVD) with physics-based partitioning analysis to achieve accurate classification of events. Special consideration is given to discerning a sophisticated fault-replay or fault spoofing attack from actual faults. A software-based cybersecurity testbed has been developed for rigorous testing of the algorithm. The algorithm is further validated using simulated synchrophasor datasets obtained from a MinniWECC 63-bus test system. Results show that the algorithm can successfully detect fault-replay attacks even when over half of the PMUs are compromised.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. Here, we also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

42 ENGINEERING↗
DOE OSTI2018

Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads

This paper considers power distribution networks with distributed energy resources and designs an incentive-based algorithm that allows the network operator and customers to pursue given operational and economic objectives while concurrently ensuring that voltages are within prescribed limits. Heterogeneous DERs with continuous and discrete control commands are considered. We address four major challenges: discrete decision variables, non-convexity due to a Stackelberg game structure, unavailability of private information from customers, and asynchronous operation. Starting from a non-convex setting, we develop a distributed stochastic dual algorithm that solves a relaxed problem, and prove that the proposed algorithm achieves the global optimal solution of the original problem on average. Feasible values for discrete decision variables are also recovered. Stability of the algorithm is analytically established and numerically corroborated.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. Here, the selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

An algorithm to calculate a collapsed arc dose matrix in volumetric modulated arc therapy

Purpose: The delivery of volumetric modulated arc therapy (VMAT) is more complex than other conformal radiotherapy techniques. In this work, the authors present the feasibility of performing routine verification of VMAT delivery using a dose matrix measured by a gantry mounted 2D ion chamber array and corresponding dose matrix calculated by an inhouse developed algorithm.Methods: Pinnacle, v9.0, treatment planning system (TPS) was used in this study to generate VMAT plans for a 6 MV photon beam from an Elekta-Synergy linear accelerator. An algorithm was developed and implemented with inhouse computer code to calculate the dose matrix resulting from a VMAT arc in a plane perpendicular to the beam at isocenter. The algorithm was validated using measurement of standard patterns and clinical VMAT plans with a 2D ion chamber array. The clinical VMAT plans were also validated using ArcCHECK measurements. The measured and calculated dose matrices were compared using gamma ({gamma}) analysis with 3%/3 mm criteria and {gamma} tolerance of 1.Results: The dose matrix comparison of standard patterns has shown excellent agreement with the mean {gamma} pass rate 97.7 ({sigma}= 0.4)%. The validation of clinical VMAT plans using the dose matrix predicted by the algorithm and the corresponding measured dose matrices also showed good agreement with the mean {gamma} pass rate of 97.6 ({sigma}= 1.6)%. The validation of clinical VMAT plans using ArcCHECK measurements showed a mean pass rate of 95.6 ({sigma}= 1.8)%.Conclusions: The developed algorithm was shown to accurately predict the dose matrix, in a plane perpendicular to the beam, by considering all possible leaf trajectories in a VMAT delivery. This enables the verification of VMAT delivery using a 2D array detector mounted on a treatment head.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

SU-E-T-67: Clinical Implementation and Evaluation of the Acuros Dose Calculation Algorithm

Purpose: The main aim of the current study is to present a detailed description of the implementation of the Acuros XB Dose Calculation Algorithm, and subsequently evaluate its clinical impacts by comparing it with AAA algorithm. Methods: The source models for both Acuros XB and AAA were configured by importing the same measured beam data into Eclipse treatment planning system. Both algorithms were evaluated by comparing calculated dose with measured dose on a homogeneous water phantom for field sizes ranging from 6cm × 6cm to 40cm × 40cm. Central axis and off-axis points with different depths were chosen for the comparison. Similarly, wedge fields with wedge angles from 15 to 60 degree were used. In addition, variable field sizes for a heterogeneous phantom were used to evaluate the Acuros algorithm. Finally, both Acuros and AAA were tested on VMAT patient plans for various sites. Does distributions and calculation time were compared. Results: On average, computation time is reduced by at least 50% by Acuros XB compared with AAA on single fields and VMAT plans. When used for open 6MV photon beams on homogeneous water phantom, both Acuros XB and AAA calculated doses were within 1% of measurement. For 23 MV photon beams, the calculated doses were within 1.5% of measured doses for Acuros XB and 2% for AAA. When heterogeneous phantom was used, Acuros XB also improved on accuracy. Conclusion: Compared with AAA, Acuros XB can improve accuracy while significantly reduce computation time for VMAT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-131: Dosimetric Impact and Evaluation of Different Heterogenity Algorithm in Volumetric Modulated Arc Therapy Plan for Stereotactic Ablative Radiotherapy Lung Treatment with the Flattening Filter Free Beam

Purpose: The present study aimed to investigate the dosimetric impacts of the anisotropic analytic algorithm (AAA) and the Acuros XB (AXB) plan for lung stereotactic ablative radiation therapy using flattening filter-free (FFF) beam. We retrospectively analyzed 10 patients. Methods: We retrospectively analyzed 10 patients. The dosimetric parameters for the target and organs at risk (OARs) from the treatment plans calculated with these dose calculation algorithms were compared. The technical parameters, such as the computation times and the total monitor units (MUs), were also evaluated. Results: A comparison of DVHs from AXB and AAA showed that the AXB plan produced a high maximum PTV dose by average 4.40% with a statistical significance but slightly lower mean PTV dose by average 5.20% compared to the AAA plans. The maximum dose to the lung was slightly higher in the AXB compared to the AAA. For both algorithms, the values of V5, V10 and V20 for ipsilateral lung were higher in the AXB plan more than those of AAA. However, these parameters for contralateral lung were comparable. The differences of maximum dose for the spinal cord and heart were also small. The computation time of AXB was found fast with the relative difference of 13.7% than those of AAA. The average of monitor units (MUs) for all patients was higher in AXB plans than in the AAA plans. These results indicated that the difference between AXB and AAA are large in heterogeneous region with low density. Conclusion: The AXB provided the advantages such as the accuracy of calculations and the reduction of the computation time in lung stereotactic ablative radiotherapy (SABR) with using FFF beam, especially for VMAT planning. In dose calculation with the media of different density, therefore, the careful attention should be taken regarding the impacts of different heterogeneity correction algorithms. The authors report no conflicts of interest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-137: Dosimetric Validation for Pinnacle, Acuros, AAA, and Brainlab Algorithms with Induced Inhomogenieties

Purpose: To compare the dosimetric accuracy of the Eclipse 11.0 Acuros XB and Anisotropic Analytical Algorithm (AAA), Pinnacle-3 9.2 Collapsed Cone Convolution, and the iPlan 4.1 Monte Carlo (MC) and Pencil Beam (PB) algorithms using measurement as the gold standard. Methods: Ion chamber and diode measurements were taken for 6, 10, and 18 MV beams in a phantom made up of slab densities corresponding to solid water, lung, and bone. The phantom was setup at source-to-surface distance of 100 cm, and the field sizes were 3.0 × 3.0, 5.0 × 5.0, and 10.0 × 10.0 cm2. Data from the planning systems were computed along the central axis of the beam. The measurements were taken using a pinpoint chamber and edge diode for interface regions. Results: The best agreement between data from the algorithms and our measurements occurs away from the slab interfaces. For the 6 MV beam, iPlan 4.1 MC software performs the best with 1.7% absolute average percent difference from measurement. For the 10 MV beam, iPlan 4.1 PB performs the best with 2.7% absolute average percent difference from measurement. For the 18 MV beam, Acuros performs the best with 2.0% absolute average percent difference from measurement. It is interesting to note that the steepest drop in dose occurred the at lung heterogeneity-solid water interface of the18 MV, 3.0 × 3.0 cm2 field size setup. In this situation, Acuros and AAA performed best with an average percent difference within −1.1% of measurement, followed by iPlan 4.1 MC, which was within 4.9%. Conclusion: This study shows that all of the algorithms perform reasonably well in computing dose in a heterogeneous slab phantom. Moreover, Acuros and AAA perform particularly well at the lung-solid water interfaces for higher energy beams and small field sizes.

60 APPLIED LIFE SCIENCES↗