Linear Combination of Hamiltonian Simulation for Nonunitary Dynamics with Optimal State Preparation Cost
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Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore critical loads with local non-utility resources. Distribution system operators (DSOs) need to determine the critical loads to be restored, considering limited resources and distribution facilities. Several studies on resiliency have been conducted for the restoration of distribution systems. However, the inherent asynchronous characteristic on the information availability has not been incorporated. With incomplete and asynchronous information, decisions may be made that result in underutilization of generation resources. In this paper, a new distribution system restoration approach is proposed, considering uncertain devices and associated asynchronous information. It uses a two-module architecture that efficiently optimizes restoration actions using a binary linear programming model and evaluates their feasibility with unbalanced optimal power flow. Networked microgrids are included in the model. The IEEE 123-node test feeder is used for validation. Results show that asynchronous messages may affect the restoration actions significantly and the impacts can be mitigated by the proposed decision support tool for the DSOs.
Purpose: In premise of uninfluenced to dose distribution of tumor target and organ at risk(OAR) in cervical cancer,area of segment fields was changed to increase efficacy and optimize treatment method by designing different plan of intensity modulated radiotherapy(IMRT). Methods: 12 cases of cervical cancer were confirmed in pathology and treated with step and shoot IMRT. Dose of PTV was 50Gy/25fractions. Every patient was designed 9 treatment plans of IMRT by Pinnacle 8.0m planning system,each plan was used with 9 beams of uniform distribution and fixing incidence direction(200°,240°,280°,320°,0°,40°,80°,120°and 160°respectively),and designed for delivery on Elekta Synergy linear accelerator. All plans were optimized with the direct machine parameter optimization(DMPO) algorithm using the same set of optimization objectives. Number of maximum segment field was defined at 80 and minimum MU in each segment was 5MU,and minimal segment area was 2*1cm{sup 2},2*2cm{sup 2},3*3cm{sup 2},4*4cm{sup 2},5*5cm{sup 2},6*6cm{sup 2},7*7cm{sup 2},8*8cm{sup 2}and 9*9cm{sup 2},respectively.Coverage,homogeneity and conformity of PTV,sparing of OAR, MU and number of segment were compared. Results: In this group, mean volume of PTV was 916.8±228.7 cm{sup 3}. Compared with the area of minimal segment field increased from 2*1cm{sup 2} to 9*9 cm{sup 2},the number of mean MU was decreased from 1405±170 to 490±47 and the number of segment field was reduced from 76±4 to 39±7 respectively(p<0.05). When the limit of minimal segment area was increased from 2*1cm{sup 2} to 7*7 cm{sup 2},dose distribution of PTV,OAR,CI,HI and V{sub 2} {sub 3} were not different (p>0.05),but when the minimal segment area was 8*8 cm{sup 2} and 9*9 cm{sup 2},they were changed compared with 7*7 cm{sup 2} and below(p<0.05). Conclusion: The minimal segment field of IMRT plan designed by Pinnacle 8.0m planning system in cervical carcinoma should be enlarge reasonably and minimal segment area of 7*7 cm{sup 2} was recommend.
Highly sensitive nanosensors with high spatial resolution provide the necessary features for high accuracy imaging of isolated magnetic nanoparticles. In this work, we report the fabrication and characterization of MgO-barrier magnetic tunnel junction nanosensors, with two exchange-pinned electrodes. The perpendicular magnetization configuration for field sensing is set using a two-step annealing process, where the second annealing temperature was optimized to yield patterned sensors responses with improved linearity. The optimized circular nanosensors show sensitivities up to 0.1%/Oe, larger than previously reported for nanometric sensors and comparable to micrometric spin-valves. Our strategy avoids the use of external permanent biasing or demagnetizing fields (large for smaller structures) to achieve a linear response, enabling the control of the linear operation range using only the stack and thus providing a small footprint device.
One of the unique features of JLab's Medium-energy Electron-Ion Collider (MEIC) is a full-acceptance detector with a dedicated, small-angle, high-resolution detection system, capable of covering a wide range of momenta (and charge-to-mass ratios) with respect to the original ion beam to enable access to new physics. We present an interaction region design developed with close integration of the detection and beam dynamical aspects. The dynamical aspect of the design rests on a symmetry-based concept for compensation of non-linear effects. The optics and geometry have been optimized to accommodate the detection requirements and to ensure the interaction region's modularity for ease of integration into the collider ring lattices. As a result, the design offers an excellent detector performance combined with the necessary provisions for non-linear dynamical optimization.
The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results. Here, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. A set of non-dominant solutions can be obtained to form the Pareto front. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system. The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). Finally, this demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and efficiency.
Significance Currently, the effect of branching of nanoparticles tens of nanometers in size on the mechanical properties of structural composites is not well understood due to the limited availability of branched nanoscale fillers. We report that branched nanofillers have the potential for optimization of nanocomposite Young’s modulus over their linear counterparts. Lattice spring model simulations reveal that the mechanism for this improvement involves the ability of branched nanoparticles to optimize the ratio of both filler and stiff interfacial bonds aligned with the tensile axis, as opposed to linear nanoparticles, which optimize only the filler bond orientation when parallel to the stretching axis. We believe this information could inform the design of nanocomposites with optimized mechanical properties for a variety of structural applications.
Purpose: To evaluate VMAT treatment plans generated with HD120 MLC and Millennium 120 MLC between two matched linacs and to determine if one can back up the other. Methods: The 6x photon beams are matched for our Varian TrueBeam STx and Trilogy linacs, which are equipped with HD120 MLC and Millennium 120 MLC, respectively. Three prostate and three brain VMAT plans were used for the evaluation. Five plans (three prostate and two brain plans) were originally generated with the TrueBeam STx and re-computed with the Trilogy. One brain plan was evaluated the other way around. For each plan, the PTV coverage of V95 was made the same between two linacs. The dosimetric differences associated with the plans were compared using: 1) Percentage mean dose differences to the PTV, 2) Homogeneity index, HI = (Dmax − Dmin)/Dmean for the PTV. For prostate plans, the mean dose differences to the rectum were evaluated. While for brain plans, the percentage max dose differences to the lenses (left and right lens) were evaluated. Results: For three prostate plans, the average of the percentage mean dose differences to the PTV was 0.5 ± 0.1% and the HI was 0.1 ± 0.0%. The average of the percentage mean dose difference to the rectum was 3.5 ± 0.5%. For three brain plans, the average of the percentage mean dose differences to the PTV was 0.2 ± 1.1% and the HI was 0.2 ± 0.1%. The average of the percentage max dose differences to the lenses was 22.9 ± 4.0%. Conclusion: For prostate VMAT plans, changing the treatment from the TrueBeam STx to the Trilogy does not necessarily need re-optimization. But for brain plans, in order to minimize dose to the lenses, it is recommended to re-optimize the plan if changing the treatment between these two linacs.
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Purpose: To quantitatively compare low dose spillage outside of PTV edge in arc therapy modalities Methods: The machines used in the study are Tomotherapy Hi-Arc and Varian 21EX with millennium120 MLC. TPS are TomoPlaning and RayStation for VMAT, respectively. The phantom is a 30cm diameter cylindrical solid water (TOMOTHERAPY, TOMOPHANTOM ASSY). The PTV is 4cm length with ellipsoidal sectional shape with major axis=5cm, minor axis=3cm in the axial plane and reversed in the coronal plane. The PTV volume is created with interpolation. It is located at the center of the phantom. The prescribed dose is 1000x5 cGy to 95% the PTV. The isocenter is set co-centered with the PTV. EBT-3 film was used to measure iso-dose lines at the center plane. Film dosimetry is performed with the RIT, v6.2. Results: the study shows: (1) dose falloff gradient is usually uneven, depending on the PTV shape in the gantry rotation plane. For an elliptical shape, the low dose spillage is wider in the minor axis direction than that in the major axis direction. The more a shape is closer to circular, the more even gradient is all directions; (2)for a circular shape (CAX plane in this study), the maximum dose in % of Rx dose at 2cm from PTV is 55% for Tomo, vs. 70% for VMAT (3) the most rapid dose falloff rang is between 95%–80% IDL for both modalities. Conclusion: Tomo has more rapid dose falloff outside of PTV. In some areas, the gradient is double for Tomo helical than that for LINAC VMAT at same points. Future work will examine the differences between optimization of doses and inherent delivery limitations.
The NIMROD code is used to perform simulations of MHD activity in the HBT-EP tokamak, including the effect of a resistive wall. Linear simulations are conducted to scan current and pressure profiles to establish self-consistent baseline equilibria that result in MHD instabilities with low error between experimental diagnostics and synthetic equivalents. A family of such equilibria is found with relatively peaked current and pressure profiles. Nonlinear simulations are performed using the optimal equilibrium, which is linearly unstable to a Resistive Wall Mode—stable in the ideal wall limit and with an Alfvénic growth rate in the no-wall limit. Using resistive wall boundary conditions, nonlinear Hall MHD simulations yield a rotating, saturated n=1 mode similar to the type observed experimentally in HBT-EP discharges. An n=1 mode around the q=2 surface mediates a localized axisymmetric perturbation that flattens the plasma current from a linearly unstable state to a 3D, rotating, stable state. During the saturated period, periodic changes in inter-diagnostic phase lag allow for qualitative estimations of effective, macroscopic transport coefficients used in MHD closure models and could be extended to model post-disruption MHD structure rotation after thermal quenches seen in HBT-EP.
This paper proposes a new methodology to capture the impact of fast moving clouds on utility power demand charges observed in microgrids with photovoltaic (PV) arrays, generators, and electrochemical energy storage. It consists of a statistical approach to introduce sub-hourly events in the hourly economic accounting process. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state of the art mixed integer linear model used to optimally size DER in decentralized energy systems. Results suggest that previous iterations of DER-CAM could undersize battery capacities. The improved model depicts more accurately the economic value of PV as well as the synergistic benefits of pairing PV with storage.
It is shown how the electronic equations of motion in extended Lagrangian Born–Oppenheimer molecular dynamics simulations can be integrated using low-rank approximations of the inverse Jacobian kernel. This kernel determines the metric tensor in the harmonic oscillator extension of the Lagrangian that drives the evolution of the electronic degrees of freedom. The proposed kernel approximation is derived from a pseudoinverse of a low-rank estimate of the Jacobian, which is expressed in terms of a generalized set of directional derivatives with directions that are given from a Krylov subspace approximation. The approach allows a tunable and adaptive approximation that can take advantage of efficient preconditioning techniques. The proposed kernel approximation for the integration of the electronic equations of motion makes it possible to apply extended Lagrangian first-principles molecular dynamics simulations to a broader range of problems, including reactive chemical systems with numerically sensitive and unsteady charge solutions. This can be achieved without requiring exact full calculations of the inverse Jacobian kernel in each time step or relying on iterative non-linear self-consistent field optimization of the electronic ground state prior to the force evaluations as in regular direct Born–Oppenheimer molecular dynamics. We note the low-rank approximation of the Jacobian is directly related to Broyden’s class of quasi-Newton algorithms and Jacobian-free Newton–Krylov methods and provides a complementary formulation for the solution of nonlinear systems of equations.
Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms.
The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.
Purpose: To demonstrate the commissioning of constant dose rate volumetric modulated arc therapy (VMAT) in the Raystation treatment planning system for a Varian Clinac iX with Exact couch. Methods: Constant dose rate (CDR) VMAT is an option in the Raystation treatment planning system, enabling VMAT delivery on Varian linacs without a RapidArc upgrade. Raystation 4.7 was used to commission CDR-VMAT for a Varian Clinac iX. Raystation arc model parameters were selected to match machine deliverability characteristics. A Varian Exact couch model was added to Raystation 4.7 and commissioned for use in VMAT optimization. CDR-VMAT commissioning checks were performed on the linac, including patient-specific QA measurements for 10 test patients using both the ArcCHECK from Sun Nuclear Corporation and COMPASS from IBA Dosimetry. Multi-criteria optimization (MCO) in Raystation was used for CDR-VMAT planning. Results: Raystation 4.7 generated clinically acceptable and deliverable CDR-VMAT plans for the Varian Clinac. VMAT plans were optimized including a model of the Exact couch with both rails in the out positions. CDR-VMAT plans generated with MCO in Raystation were dosimetrically comparable to Raystation MCO-generated IMRT plans. Patient-specific QA measurements with the ArcCHECK on the couch showed good agreement with the treatment planning system prediction. Patient-specific, structure-specific, multi-statistical parameter 3D QA measurements with gantry-mounted COMPASS also showed good agreement. Conclusion: Constant dose rate VMAT was successfully modeled in Raystation 4.7 for a Varian Clinac iX, and Raystation’s multicriteria optimization generated constant dose rate VMAT plans which were deliverable and dosimetrically comparable to IMRT plans.
Purpose: To investigate a spatiotemporally optimal radiotherapy prescription scheme and its potential benefit for glioblastoma (GBM) patients using the proliferation and invasion (PI) glioma model. Methods: Standard prescription for GBM was assumed to deliver 46Gy in 23 fractions to GTV1+2cm margin and additional 14Gy in 7 fractions to GTV2+2cm margin. We simulated the tumor proliferation and invasion in 2D according to the PI glioma model with a moving velocity of 0.029(slow-move), 0.079(average-move), and 0.13(fast-move) mm/day for GTV2 with a radius of 1 and 2cm. For each tumor, the margin around GTV1 and GTV2 was varied to 0–6 cm and 1–3 cm respectively. Total dose to GTV1 was constrained such that the equivalent uniform dose (EUD) to normal brain equals EUD with the standard prescription. A non-stationary dose policy, where the fractional dose varies, was investigated to estimate the temporal effect of the radiation dose. The efficacy of an optimal prescription scheme was evaluated by tumor cell-surviving fraction (SF), EUD, and the expected survival time. Results: Optimal prescription for the slow-move tumors was to use 3.0(small)-3.5(large) cm margins to GTV1, and 1.5cm margin to GTV2. For the average- and fast-move tumors, it was optimal to use 6.0cm margin for GTV1 suggesting that whole brain therapy is optimal, and then 1.5cm (average-move) and 1.5–3.0cm (fast-move, small-large) margins for GTV2. It was optimal to deliver the boost sequentially using a linearly decreasing fractional dose for all tumors. Optimal prescription led to 0.001–0.465% of the tumor SF resulted from using the standard prescription, and increased tumor EUD by 25.3–49.3% and the estimated survival time by 7.6–22.2 months. Conclusion: It is feasible to optimize a prescription scheme depending on the individual tumor characteristics. A personalized prescription scheme could potentially increase tumor EUD and the expected survival time significantly without increasing EUD to normal brain.
We have developed new measures of errors for numerical shock. The new approach is based on analysis of the structure function, and separation of the errors related to oscillations and shock width, which also include error in the position of the ”center” of the numerical shock. We have demonstrated that those measures correctly characterize the numerical solution. We introduced an objective function in, which both types of errors are weighted, and presented optimal values of the coefficients of the linear and quadratic viscosity for different weights and different Mach numbers.