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Combined Cycle Integrated Thermal Energy Storage “CiTES” (Final Scientific/Technical Report)

The Phase I of this project confirmed the technical feasibility of a Combined Cycle integrated Thermal Energy Storage “CiTES” system, calculated the key performance parameters like power efficiency and costs, and proved its commercial value with full-year simulations for several US electricity markets with high degree of variable renewable generation and volatile hourly electricity prices. The core element of this project is the Electro Thermal Energy Storage (ETES) technology from Siemens Gamesa Renewable Energy GmbH, using thermally stable and inexpensive volcanic rocks as storage material and air as heat transfer medium. This technology is backed by more than 10 years of experience and a 440MMBTU (130MWh-th) pilot plant in Hamburg, Germany, which is in operation since 2019. The integration of this thermal storage in an existing combined cycle power plant (CCPP) is typical power plant technology without any major technology risks. It allows the storage of inexpensive renewable energy during times of surplus renewable generation and the discharge of this energy in times of high energy demand when the fossil plant is in operation. This supplements the fossil power generation with CO2-emission-free energy. The secondary effect of the CiTES system is that a small part of the stored thermal energy is used to keep the heat recovery steam generator (HRSG) and steam turbine (ST) of the combined cycle power plant in hot and ready-to-start condition. This enables the plant to start rapidly when fossil generation is required to satisfy demand as soon variable generation drops off in the evenings or during cloud cover and calm wind periods. Without pre-warming of the HRSG and ST, the CCPP would need several hours for a cold or warm start, burn a lot of gas and release high NOx emissions during start and wouldn’t be able to use the short times of high energy prices in an efficient or economical manner. The economic parameters of CiTES were determined by a full year “8760” simulation using a data set calculation for each of the hours of the year, and historical electricity and gas prices. For consistency, the simulations were focused on the pre-COVID year 2019. The financially most attractive markets were in the Energy Reliability Council of Texas (ERCOT) region, which allowed substantial value generation with arbitrage (charge with cheap energy during renewable surplus times and discharge when energy is needed and expensive). The improvement of flexibility with the CiTES system by pre-heating and warm-keeping of the CCPP allowed for additional power generation during short time periods when demand is high but renewable generation is down; when the hourly energy prices are highest in these markets. The simulations are based on 2019 data, when ERCOT had 27GW of installed photovoltaic (PV) and wind generation. They showed that the created revenue with the prototypically sized CiTES system of 1,000MMBTU (300MWh-th) falls a little bit short of what is expected from a commercially viable investment. The system has optimization opportunities for cost reduction and increased effectiveness which will be realized during a potential Phase II Pre-FEED study following this project. Furthermore, it is safe to assume that a lot of renewable generation capacity will be added all over the US in the coming years. As an example, ERCOT is predicting to more than double its renewable generation from 27GW in 2019 to a forecasted 63GW in 2023. This will increase the amount of renewable overproduction exponentially. This rapid increase of local overproduction and the need to curtail renewable generation is well documented by the California ISO (www.CAISO.com / managing oversupply). However, the simulations also revealed a weakness in the structure of the electricity markets in the US. More specifically, when electricity prices are very low and approaching negative levels, the owners of Variable Renewable Energy (VRE) will curtail a part of their facility to stabilize the price by reducing supply. This results in a situation in which storage facilities, which are integrated in existing fossil assets and don’t have the behind-the-meter benefit of a VRE, won’t be able to purchase low cost – otherwise curtailed – renewable energy off the grid. A special tariff, which motivates VRE owners to sell otherwise curtailed renewable energy to storage facilities (Hydrogen, thermal, pumped hydro, etc.) can solve this issue. The implementation of such a regulating tariff by Independent System Operators, thus avoiding renewable curtailment, is a pre condition for successful commercialization for renewable energy storage technologies. With this advancement of design and technology and improvements in the market environment, it can be expected that the Combined Cycle integrated Thermal Energy Storage proves itself as an important innovation to keep highly efficient, natural gas-based power generation economically successful and relevant for the power industry in the United States of America.

Wolf, Thorsten↗

Report of METL Mutual Inductance Level Sensor Development for Use in Liquid Metals – FY2023

A robust mutual inductance level sensor (MILS) for use in high temperature liquid metals has been developed at Argonne National Laboratory (ANL). The current design utilizes mineral insulated cables with a 300-series stainless steel sheath, magnesium oxide insulation, and a single copper conductor. Two coils are wrapped on a common core made of a 300-series stainless steel tube. One coil is energized with an alternating current (AC) source, and this electromagnetically couples with the second coil to generate an induced voltage. The voltage is measured to determine the mutual induction between the coils, and the mutual inductance measurement can be used to determine the level of a nearby liquid metal volume. The MILS can be located in an isolating thimble that is fully sealed to a vessel containing liquid metal for ease of maintenance and replacement. When the primary coil is energized with the AC source, the coil not only electromagnetic (EM) couples with the second coil, but also the surrounding liquid metal. The EM coupling with the surrounding liquid metal reduces the EM coupling with the second coil, producing an inverse-linear relationship between liquid metal level and secondary coil voltage. This has all been demonstrated at the Mechanisms Engineering Test Loop (METL) liquid sodium facility at ANL. The most current iteration of the MILS system is the MILS-MK-II. This sensor system has been commissioned in a non-sodium test stand where calibrations were performed using a sodium analog. The calibrations proved to be highly linear and repeatable. The MILS-MK-II has been installed in the METL expansion tank where high temperature sodium tests have been performed. The MILS-MK-II has been calibrated against a known standard at temperature of 300°C, and the calibrations have proved to be highly linear and repeatable. The calibrated MILS-MK-II has been in operation in the METL facility for several thousands of hours at temperatures around 300°C. The experimental data has been used to develop and validate electromagnetic finite element models in COMSOL Multiphysics, and now these models can be used to advance the development of the sensor system. Next steps will include temperature compensation to allow for operation at various temperatures. Additionally, efforts to incorporate a self-calibration methodology are underway.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Intelligent Proppants for Hydraulic Fracture Diagnostics

This Phase 1 Report details our Phase 1 progress in proving the feasibility of a highly integrated, highly miniaturized sensor ball. The ball contains sensors, analog to digital converters, memory, a clock, and an acoustic communications module all integrated onto a single silicon chip. Phase 2 is to focus on the application specific integrated circuit (ASIC). Phase 1 piecewise proves the feasibility of this innovation by examining a few critical subsystems including the communications module and inertial sensor performance. Acoustic simulations of an acoustically active and traceable proppant are also performed and summarized in this report.

01 COAL, LIGNITE, AND PEAT↗

Charge per Micro-Pulse Calculation for Ideal Lujan and WNR beams

A complete redesign of the LANSCE front-end is currently under development for the Los Alamos Modernization Project (LAMP). This includes the replacement of the traditional Cockroft-Walton injection system to the newer radiofrequency quadrupole (RFQ) standard. LANSCE accelerates both H- and H+ beams and therefore requires an unconventional application for RFQ systems. A new facility could consider using an independent RFQ for each beam species. However, with the tight space of the LANSCE injector facility, it would prove difficult to instrument. Our initial studies have shown that a single RFQ could be used. The negatively- and positively-charged direct current (DC) beams are shaped, bunched, and accelerated out of phase to each other, with very little interaction. This would work for all possible H+ beam gates and most H- Beam gates. However, a challenge arises with the Weapons Neutron Research (WNR) beam structure that requires the acceleration of bunched beams at higher charge density per pulse. The Long Bunch Enable Gate (LBEG) used for the Lujan center is the best example of a standard injector beam gate. The beam gate, or Macro-Pulse (MP), is 625-us long, separated by at least 8.3 ms. A chopper is used to dice the MP into 1750 mini-Pulses (mP), which are 290-ns wide. After chopping, the MP contains 100,000 micro-Pulses (uP), which are generated by the linac acceleration structure modulated at 201.25 MHz; that is, the micro-Pulses are separated 5 ns apart from each other. The H+ beam structure will be very similar once a chopper is added to its transport. However, the Multi-Pulse Enable Gate (MPEG) used for WNR is significantly different in structure. In this case, the mP is chopped to be only 35-ns wide. This width is then rotated into a single 201.25 MHz RF bucket using the Low Frequency Buncher (LFB). This is proving difficult for the RFQ design in the LAMP upgrade. We began investigating the amount of current sent to WNR for two reasons. Firstly, we wanted to find a solution to avoid using the Low Frequency Buncher to simplify the LAMP RFQ design. It is simple to inject and accelerate a 35 mA DC beam in the LAMP RFQ (this is done in numerous accelerator facilities). However, injecting a short bunch with a larger current (once compressed to 5 ns to meet the RFQ frequency) may be difficult. Second, we have a technique called PSR2WNR in which we do not employ the Low Frequency Buncher, but rather accumulate 5 LBEG uP (5 ns) in the PSR and deliver the accumulated beam to WNR every 1.8 us. As a result, we could provide to WNR with five accumulated LBEG uPs every shot. The goal here is to create a rapid kicker with a frequency response of 555 kHz. DARHT-II and other accelerator facilities have constructed kickers of this type for other uses, and our engineers consider this technology as feasible.

43 PARTICLE ACCELERATORS↗

Highly Efficient Vacuum Smelting of Aluminum

The goal of this SBIR project was to develop and demonstrate an innovative aluminum smelting technology that will significantly increase the range of aluminum alloys that can be manufactured from recycling of scrap aluminum. This will result in reduction of need for primary aluminum with corresponding 95% energy and CO 2 emission benefits. The proposed melting of pre-sorted aluminum alloys using UHV’s patented high throughput sorter funded by ARPA-E METALS program in an energy efficient vacuum furnace avoids the typical 5% metal loss due to oxidation of molten metal. In addition, pre-sorting scrap into different categories (cast, wrought, extrusion and aerospace) allows lower cost production of high value aluminum alloys without the need for adding alloying elements to primary aluminum. During this SBIR project, pure aluminum and several different commercial alloys were procured and melted in a laboratory furnace in vacuum, CH4 and air environments. The slag and oxidation formation were measured using weight and cross-sectional scanning electron microscope. The results otained were mixed and did not show direct correlation between furnace pressure and oxidation. We believe that the results were tainted due to small furnace size and non-uniformity of oxidation layer. A more thorough investigation is required to prove the advantages of vacuum smelting which could not be performed due to the COVID-19 lockdowns during this project. The theoretical fact remains that in the absence of oxygen in the furnace, there should be no oxide after melting, however the team could not prove it experimentally, and thus decided to dis-continue the project.

36 MATERIALS SCIENCE↗

Simple self-consistent method for excited states in density functional theory to characterize defect-derived behavior in wide-band-gap-based microelectronic materials

This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.

36 MATERIALS SCIENCE↗

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Convergent Manufacturing of Large-Scale Components for Nuclear Applications, via Additive Manufacturing and Powder Metallurgy Hot Isostatic Pressing

Powder metallurgy (PM)–hot isostatic pressing (PM-HIP) has long been recognized as a powerful route for producing fully dense, near net shape metallic components. By consolidating powders under high temperature and pressure, HIP provides isotropic properties, uniform microstructures, and scalability to complex geometries that are vital for sectors such as aerospace, energy, and nuclear power. Yet despite these advantages, the technology has remained constrained by costly trial and error canister fabrication, limitations of conventional forging, and incomplete knowledge about how the canister design influences final part properties. Additive manufacturing (AM), by contrast, thrives on design freedom and geometric flexibility but struggles with speed, scalability, and cost when applied to very large structures. The research presented in this report investigated how a convergent manufacturing approach, combining AM with PM-HIP, can merge the strengths of both technologies, leveraging AM’s flexibility for canister design and HIP’s consolidation capability to deliver reliable, large, and complex parts. The work progressed through three case studies that built on one another in scale and complexity. Small cylindrical canisters fabricated by conventional methods, laser powder bed fusion, and directed energy deposition were filled with stainless steel powders and subjected to HIP. The resulting parts demonstrated near-full density and mechanical properties on par with wrought stainless steel, showing for the first time that AM canisters can be a direct substitute for conventional ones without sacrificing quality. The next step involved a medium-scale, noncentrosymmetric T-valve, which is an enclosed, multibranch geometry that tested the limits of AM + PM-HIP integration. The T-valve achieved predictable shrinkage and uniform densification, confirming feasibility for enclosed designs. However, this study also revealed oxide inclusions and interfacial challenges at the AM + HIP boundary, underscoring the critical importance of controlling interface chemistry and employing robust, in situ strategies, such as melt pool monitoring and thermal monitoring, coupled with nondestructive evaluation techniques such as x-ray computed tomography. Finally, the effort culminated in fabricating a large-scale impeller weighing nearly 2000 lb and spanning 5 ft in diameter. Produced via multirobot wire arc AM and hot isostatic pressed to near-full density, the impeller validated industrial-scale feasibility. Predictive models closely matched experimental shrinkage, tensile properties were spatially uniform across the component, and the AM + PM-HIP interface proved mechanically sound despite the presence of oxide-decorated prior particle boundaries. This large-scale demonstration is a major milestone, showing that hybrid AM + PM‑HIP can reliably deliver components at reactor-relevant scales. Collectively, these studies charted a logical pathway: small-scale work built scientific confidence, medium-scale work highlighted opportunities and challenges, and large-scale work proved industrial impact. The overarching conclusion of this report is that AM + PM-HIP should not be seen as a replacement for forging but as a complementary pathway that provides the US with flexibility, resilience, and new options for manufacturing nuclear-grade components. Looking ahead, several directions emerge as critical to sustaining progress. Predictive modeling must become faster, more accessible, and more accurate, with digital twins and machine learning reducing reliance on trial and error. Powders and alloys must be optimized for HIP, with improved cleanliness, reduced oxides, and tailored chemistries that enhance creep, fatigue, and irradiation resistance. Interfaces between AM and HIP regions must be better engineered through coatings, machining strategies, and surface treatments to mitigate oxide formation and ensure reliable bonding to explore opportunities for HIP of targeted compositional parts, as well as multimaterial HIP cladding applications. Monitoring and nondestructive evaluation need to expand, incorporating multimodal sensors, x-ray computed tomography, and real-time data integration through platforms such as Pelican. At the same time, the pathway to industrial adoption requires techno-economic analysis, machinability studies, and qualification frameworks aligned with industry and regulatory standards. Finally, workforce and academic engagement must be strengthened. Programs that train technicians and engineers for US Navy and US Department of Energy manufacturing challenges should be paired with academic partnerships to support fundamental research, with open sharing of non-export-controlled data to accelerate innovation and build the next generation of experts. In conclusion, this report demonstrates that hybrid AM + PM-HIP is scientifically viable and strategically important. By combining the design agility of AM with the consolidation strength of HIP and embedding modeling, monitoring, and workforce development, this approach provided a transformative new capability for US manufacturing. The path forward is clear: hybrid AM + PM-HIP is not just a promising research direction but is also potentially an industrially relevant pathway that can reshape how nuclear-grade components are designed, qualified, and deployed.

36 MATERIALS SCIENCE↗

Variable Effects of Dispersed Nanoparticles on Triboelectric Nanogenerators

Technology has recently seen a drastic physical downsizing. Wearable and small devices with lower power demands have become the norm and continue to be more prominent in daily life. With modern devices growing smaller and requiring less electricity, a power source will always be needed. Contemporary batteries are the most common means to power small electronics. However, reliance on conventional batteries may prove insufficient due to the non renewable resources (Li, Ni, Co) required to power the growing number of individual devices each person may own. Additionally, the infrastructure required to harvest and recycle the sheer number of batteries produced presents a further logistic issue to be addressed. A promising alternative to batteries is the usage of triboelectric nanogenerators (TENGs). TENGs are a class of energy harvesting devices that utilize triboelectric generation to convert mechanical/kinetic energy into electrical energy and have exhibited efficiencies up to 85 % at low frequencies. TENGs exhibit a high voltage but low current. Even with the high voltage, the low current output proves to be a significant factor preventing undoped TENGs from being commercially viable. This review will investigate factors that increase the total current produced by TENGs when nanoparticles are utilized in TENGs. Factors such as increasing porosity, surface area, surface charge density, charge storage, deep trap formation, and dielectric constant can be altered to affect the total current by impregnating nanoparticles into the polymer material will be explored.

36 MATERIALS SCIENCE↗

Variable Effects of Dispersed Nanoparticles on Triboelectric Nanogenerators

Technology has recently seen a drastic physical downsizing. Wearable and small devices with lower power demands have become the norm and continue to be more prominent in daily life. With modern devices growing smaller and requiring less electricity, a power source will always be needed. Contemporary batteries are the most common means to power small electronics. However, reliance on conventional batteries may prove insufficient due to the non renewable resources (Li, Ni, Co) required to power the growing number of individual devices each person may own. Additionally, the infrastructure required to harvest and recycle the sheer number of batteries produced presents a further logistic issue to be addressed. A promising alternative to batteries is the usage of triboelectric nanogenerators (TENGs). TENGs are a class of energy harvesting devices that utilize triboelectric generation to convert mechanical/kinetic energy into electrical energy and have exhibited efficiencies up to 85 % at low frequencies. TENGs exhibit a high voltage but low current. Even with the high voltage, the low current output proves to be a significant factor preventing undoped TENGs from being commercially viable. This review will investigate factors that increase the total current produced by TENGs when nanoparticles are utilized in TENGs. Factors such as increasing porosity, surface area, surface charge density, charge storage, deep trap formation, and dielectric constant can be altered to affect the total current by impregnating nanoparticles into the polymer material will be explored.

42 ENGINEERING↗

Compositional Reasoning for Hierarchical State Machines

Harel statecharts and its derivatives are popular graphical languages for specifying discrete control systems via hierarchical state machines. Separately, there has been a long line of work on specifying concurrent systems with process calculi which come equipped with an algebraic theory, the ability reason compositionally about various temporal properties, and strong type systems. While these two approaches to modeling systems are tantalizingly similar, the integrated reasoning principles that exist for process calculi have not been demonstrated in hierarchical state machines. A key issue is that operational theories for process calculi do not behave like control systems, and thus, there is virtually no tool support for modeling control systems with such languages. For a control system designer, bringing the integrated, more scalable reasoning from the process calculi to state-machine languages would enable the specification of more complex systems and a more modular systems development process. Our insight is that we can recover many important results from the process calculi in hierarchical state machines with local scope. We employ a structural operational semantics, which is ubiquitous in process and 𝜆-calculi but uncommon in hierarchical statemachine formalizations, to enable inductive reasoning about behavior. Taking inspiration from the structure of process calculi metatheories, we define a calculus of refinement and equivalence that we prove sound with respect to local notion of (bi)simulation. Furthermore, we prove that the calculus preserves the behavioral properties of reactivity, observational determinism, traces, and linear temporal properties. Our results are mechanized in the Rocq proof assistant.

97 MATHEMATICS AND COMPUTING↗

Technology & Design Innovations to Maximize the Reduction Effect on DCFC Unit Cost Economics (Max‐REDUCE)

With the high cost and reliability issues of DCFC infrastructure the project specifically addressed the needs of innovating on the AC to DC power conversion. We proved the integration of a new power module into existing charger units, and that theoretical educational work on single stage energy conversion is commercially viable, and a reduced cost compared to existing methods. Our A sample shown 3 PH 480VAC 60hz to 350VDC 8.5kW power transfer, and our software and simulation proved efficiency greater than 97% from 20kW to > 60kW, over the DC output voltage range 200 – 1200V. The project was stopped before we could implement B sample and execute full power testing, validation and integration into a demonstration charger (budget period 3).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra $\mathfrak{g}$ obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of $\mathfrak{g}$.

97 MATHEMATICS AND COMPUTING↗

Page curves and typical entanglement in linear optics

Bosonic Gaussian states are a special class of quantum states in an infinite dimensional Hilbert space that are relevant to universal continuous-variable quantum computation as well as to near-term quantum sampling tasks such as Gaussian Boson Sampling. In this work, we study entanglement within a set of squeezed modes that have been evolved by a random linear optical unitary. We first derive formulas that are asymptotically exact in the number of modes for the Rényi-2 Page curve (the average Rényi-2 entropy of a subsystem of a pure bosonic Gaussian state) and the corresponding Page correction (the average information of the subsystem) in certain squeezing regimes. We then prove various results on the typicality of entanglement as measured by the Rényi-2 entropy by studying its variance. Using the aforementioned results for the Rényi-2 entropy, we upper and lower bound the von Neumann entropy Page curve and prove certain regimes of entanglement typicality as measured by the von Neumann entropy. Our main proofs make use of a symmetry property obeyed by the average and the variance of the entropy that dramatically simplifies the averaging over unitaries. In this light, we propose future research directions where this symmetry might also be exploited. We conclude by discussing potential applications of our results and their generalizations to Gaussian Boson Sampling and to illuminating the relationship between entanglement and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Avoiding symmetry roadblocks and minimizing the measurement overhead of adaptive variational quantum eigensolvers

Quantum simulation of strongly correlated systems is potentially the most feasible useful application of near-term quantum computers. Minimizing quantum computational resources is crucial to achieving this goal. A promising class of algorithms for this purpose consists of variational quantum eigensolvers (VQEs). Among these, problem-tailored versions such as ADAPT-VQE that build variational ansätze step by step from a predefined operator pool perform particularly well in terms of circuit depths and variational parameter counts. However, this improved performance comes at the expense of an additional measurement overhead compared to standard VQEs. Here, we show that this overhead can be reduced to an amount that grows only linearly with the number n of qubits, instead of quartically as in the original ADAPT-VQE. We do this by proving that operator pools of size 2 n − 2 can represent any state in Hilbert space if chosen appropriately. We prove that this is the minimal size of such "complete" pools, discuss their algebraic properties, and present necessary and sufficient conditions for their completeness that allow us to find such pools efficiently. We further show that, if the simulated problem possesses symmetries, then complete pools can fail to yield convergent results, unless the pool is chosen to obey certain symmetry rules. We demonstrate the performance of such symmetry-adapted complete pools by using them in classical simulations of ADAPT-VQE for several strongly correlated molecules. Our findings are relevant for any VQE that uses an ansatz based on Pauli strings.

Shkolnikov, V. O.↗

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.

distributed control↗

Manhattan Project 1940s research on the prompt fission neutron spectrum

We describe how the prompt fission neutron spectrum (PFNS) was determined for the Manhattan Project at Los Alamos. Early work before World War II at American and British universities is described, together with theoretical work by Feather at Cambridge and Bethe at Los Alamos. As the Manhattan Project was being planned in 1942, two experiments on natural uranium were commissioned that proved to be influential: 1) An integral experiment at Chicago by Christy and Manley that accurately determined the average PFNS spectrum energy, 2.2 ± 0.2 MeV; 2) Bloch and Staub’s Stanford cyclotron measurement of the PFNS spectrum, which obtained an average energy of 1.70 ± 0.34 MeV. These two papers, previously unavailable outside of Los Alamos, are reproduced in the Supplementary Appendix. From these data, at the beginning of the project in 1943 Serber estimated an average 235 U PFNS energy of 2 MeV, and indeed this agrees with today’s best estimate. The challenges facing the scientists involved both the availability of only very small samples of enriched uranium and plutonium targets, and fast neutron detection technologies. During the project, 235 U and 239 Pu PFNS were measured by Nicodemus and Staub. These also proved to be quite accurate and gave an average spectrum energy of 2 MeV for 235 U. [This is not reproduced in the Appendix because it was published after the war in Physical Review 89, 1288 (1953)]. New methods were developed to enable more accurate measurements, and this paper describes how the PFNS was determined surprisingly well by 1945. We end by describing the post-war measurements in the 50s, including the PFNS data used by Ford and Wheeler in their simulations in 1951, the Bonner 1952 data, the seminal 1952 Watt paper with a new empirical parametrization of the PFNS, and the accurate PFNS measurement undertaken at Los Alamos by Cranberg et al. in 1956. We compare the measurements with our best understanding today as embodied in the Evaluated Nuclear Data File ENDF/B-VIII.0. Some images from historical documents in our Los Alamos National Security Research Center (NSRC) archives are shown.

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