AI-Enhanced System Level Design and Optimization of Inertial Fusion Energy (IFE) Power Plants using the Integrated Process Model (IPM)
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A model to simulate nickel-cadmium battery performance and response in a spacecraft electrical power system energy balance calculation was developed. The voltage of the battery is given as a function of temperature, operating depth-of-charge (DOD), and battery state-of-charge. Also accounted for is charge inefficiency. A battery is modeled by analysis of the results of a multiparameter battery cycling test at various temperatures and DOD's.
We present preliminary results on the development of a fire detection system for partial and low gravity environments. A significant challenge is the design of a system with a low false alarm rate. The detection system therefore has to discriminate between combustion particles, cabin, and lunar dust. Aerosol properties are measured and cross referenced to the target detectors at the Gases and Aerosols from Smoldering Polymers (GASP) Laboratory located at the NASA Glenn Research Center. A computational fluid dynamics plume model is also employed to determine the smoke transport properties of the targeted habitats.
The development of a battery comprised of bipolar lead acid modules is discussed. The battery is designed to satisfy the requirements of the Advanced Launch System (ALS). The battery will have the following design features: (1) conventional lead acid chemistry; (2) thin electrode/active materials; (3) a thin separator; (4) sealed construction (gas recombinant); and (5) welded plastic frames for the external seal.
The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.
A summary of NASA Aerospace Flight Battery Systems Program Activities is presented. The NASA Aerospace Flight Battery Systems Program represents a unified NASA wide effort with the overall objective of providing NASA with the policy and posture which will increase the safety, performance, and reliability of space power systems. The specific objectives of the program are to: enhance cell/battery safety and reliability; maintain current battery technology; increase fundamental understanding of primary and secondary cells; provide a means to bring forth advanced technology for flight use; assist flight programs in minimizing battery technology related flight risks; and ensure that safe, reliable batteries are available for NASA's future missions.
Over a period of some 5 years Canadian Astronautics Limited (CAL) has developed a system to autonomously manage, and thus prolong the life of, secondary storage batteries. During the development, the system was aimed at the space vehicle application using nickel cadmium batteries, but is expected to be able to enhance the life and performance of any rechargeable electrochemical couple. The system handles the cells of a battery individually and thus avoids the problems of over, and under, drive that inevitably occur in a battery of cells managed by an averaging system. This individual handling also allow cells to be totally bypassed in the event of failure, thus avoiding the losses associated with low capacity, partial short circuit, and the catastrophe of open circuit. The system has an optional capability of managing redundant batteries simultaneously, adding the advantage of on line reconditioning of one battery, while the other maintains the energy storage capability of the overall system. As developed, the system contains a dedicated, redundant, microprocessor, but the capability exists to have this computing capability time shared, or remote, and operating through a data link. As adjuncts to the basic management system CAL has developed high efficiency, polyphase, power regulators for charge and discharge power conditioning.
A battery cell balancing system is operable to utilize a relatively small number of transformers interconnected with a battery having a plurality of battery cells to selectively charge the battery cells. Windings of the transformers are simultaneously driven with a plurality of waveforms whereupon selected battery cells or groups of cells are selected and charged. A transformer drive circuit is operable to selectively vary the waveforms to thereby vary a weighted voltage associated with each of the battery cells.
Inductive power transfer (IPT) systems provide a flexible, hands-free charging opportunity to electric vehicles (EV). The resonant network components and the transmitter and receiver coils are often subjected to high voltages or currents. Component aging in the compensation network and coils of resonant IPT systems is detrimental to the reliability and power transfer efficiency of the IPT system. Monitoring the component health of such multi-element complex systems requires robust optimization algorithms. This paper discusses condition monitoring of a resonant IPT system for an EV charger using a digital twin model. A hybrid estimation algorithm based on genetic algorithms and adaptive particle swarm optimization is developed to estimate the parameters of the digital twin model. Simulation results are used to verify the monitoring capabilities of the developed algorithm under various operating conditions of the IPT system.
The characteristics of NIHBES (nickel-hydrogen battery expert system) are described, with attention also given to NICBES-2 (nickel-cadmium battery expert system-2). The nickel-hydrogen battery testbed is set up almost identically to the nickel-cadmium battery testbed, with the exceptions of no battery protection and reconditioning circuits (BPRCs) and the frequency of transmission of data. The Ni-H2 testbed has no BPRCs and the data are transmitted every 30 s instead of every minute. An expert system shell was chosen to develop this particular expert system. The GoldWorks expert system shell from Gold Hill Computers was chosen for the task. NIHBES will extract the desired data and return fault diagnosis, status and advice, and decision support. Expert systems have been proven to be viable tools in the control and monitoring of space power systems. Presently, the DDAS (digital data acquisition system) monitors and controls the orbit time, and is responsible for limit checking, data acquisition, and data summaries. It is concluded that in the future control of the Hubble Space Telescope breadboard will be passed to NIHBES. NIHBES will be more beneficial to the testbed than the DDAS alone due to the limitations of the DDAS. The DDAS cannot provide long-term trend analysis, plotting capability, fault diagnosis, or advice.
This project relates to the loan of four Toyota Mirai FCEV-adv vehicles to NLR to provide a load (vehicles to fill with hydrogen) to our fueling station research facility to study hydrogen fueling infrastructure performance using 700 bar precooled hydrogen at ESIF’s Hydrogen Infrastructure Testing and Research Facility (HITRF) facility.
The Cryostat Heat Removal System (CHRS) of the Compton Spectrometer and Imager (COSI) consists of cryocooler, three transport aluminum ammonia Constant Conductance Heat Pipes (CCHP), three radiators with CCHP spreaders and Cryocooler Control Electronics (CCE) attached to a separate radiator. A Thermal Vacuum (TVAC) test was conducted at NASA Goddard Space Flight Center to characterize the integrated COSI CHRS in the flat configuration. This presentation summarizes the measured COSI CHRS performance along with lessons learned.
The US Department of Energy Office of Nuclear Energy (DOE-NE) Office of Spent Fuel and High-Level Waste Disposition is examining a set of system options and conducting supporting analyses to inform the development of an integrated waste management system, which may include one or more federal staging facilities (FSFs) for used nuclear fuel (UNF ) sited using a collaborative siting process. This paper focuses on the ongoing activities in two systems engineering and analysis work areas: (1) data and tools development, validation, and maintenance and (2) systems engineering execution. Within the first work area, the STANDARDS 5.0 UNF data and analysis tool, formerly known as UNF-ST&DARDS, is being developed as a foundational resource to assist in the management of UNF data. It has the key capability to model UNF throughout the entire back end of the fuel cycle. STANDARDS also includes several compatible analysis tools for the time-dependent characterization of UNF and related systems by interfacing with the SCALE code system for nuclear analysis and COBRA-SFS for thermal analysis. Also, within the data and tools area is the Next Generation System Analysis Model (NGSAM), which is an agent-based simulation software tool expressly designed to be capable of modeling the waste management system, including the transportation of UNF to and from a FSF. NGSAM has been developed to enable informed decision-making by providing the capability to analyze various potential system options for the management of UNF and high-level radioactive waste. Finally, in the systems engineering execution area, the team has begun to apply a disciplined systems engineering approach at the system level along with supporting analysis to guide the development of the FSF project requirements (including associated transportation infrastructure). Systems engineering principles and practices and their adaptation/application to design and development activities will ensure that the waste management system is effectively implemented as work proceeds. Other activities include investigating the implications of changes in various assumptions and parameters related to waste management systems, such as UNF acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, and different assumed facility operation start dates. Keywords: federal staging facility (FSF), used nuclear fuel (UNF), integrated waste management (IWM) system, Next Generation System Analysis Model (NGSAM), STANDARDS, systems engineering
The battery design of OAO-C (OAO-3) is given and consists of three-20 ampere hour 22 series connected cells in the battery. There are three batteries per spacecraft. The packaging configuration is described. The charging-discharging operations and the voltage potential of the battery system are discussed. Graphs are presented for the voltage limits (battery voltage versus temperature) and end of dark voltages (battery voltage versus ampere-hours discharged) of the battery system used on OAO-3. Data tables are also presented which give a summary of the battery performance and a comparison of OAO-3 with OAO A-2.
The Carryall Block 1 reentry vehicle being developed by Outpost Space utilizes a strut supported semirigid deployable heatshield. This consists of a flexible thermal protection system, a heat-resistant fabric stack, stretched over actuated spars. The advantages of a deployable heatshield include reduced heat loading and earlier deceleration in the trajectory. However, the nature of the flexible thermal protection system necessitates considering the loaded shape of the heat shield. The flexible thermal protection system will deflect under reentry loads leading to areas of higher heating rates as well as a reduced axial coefficient as compared to the nominal shape. The Carryall Block 1 is analyzed using NASA’s FUN3D and DPLR CFD solvers with a deflected shape based on the catenary equations. The aerodynamic results are found to be within a percent for both solvers and both structured and unstructured mesh types. Fluid Structure Interaction (FSI) analysis is currently a work in progress, using file I/O to communicate between FUN3D and LS-DYNA, a commercial nonlinear structural solver. Challenges in deforming the geometry, mesh, and initial results are presented here.
Current industry practices for quantifying the system strength contribution from grid-forming (GFM) resources to ensure stability of power systems dominated by inverter-based resources (IBRs) are primarily based on iterative electromagnetic transient (EMT) time-domain simulation studies. While feasible, these approaches are resource-intensive, lack scalability and intuition, and might not evaluate the system strength contribution over the entire frequency range of interest. This paper introduces a novel, frequency-domain approach to quantify system strength support provided by a GFM resource using frequency scans. The proposed method uses transfer functions from the grid voltage magnitude (V) and phase (?), respectively, to the reactive (Q) and active power (P) output of a GFM resource for quantifying its contribution to system strength. These transfer functions provide a direct measure of the ability of a GFM resource to behave as a stiff voltage source behind a reactance over a specified frequency range, enabling robust quantification of its system strength contribution. The key innovation of this work is the development of a frequency domain system strength metric called the dynamic short-circuit ratio (dSCR) that is suitable for IBR-dominated power systems and is directly related with the familiar short circuit ratio (SCR) metric. The new metric, dSCR, enables the assessment of system strength contributions from both synchronous machines and converter-based GFM resources using a unified benchmark, which is not possible with the traditional SCR metric. The paper also demonstrates how impedance scans could identify if an unstable condition observed during weak grid conditions is a result of the lack active or reactive power support or both. By leveraging the proposed frequency-domain dSCR metric for quantifying system strength contribution from GFM IBRs, the paper demonstrates targeted mitigation strategies for weak grid instabilities without resorting to repeated, time-consuming time-domain simulations. The result is a scalable and efficient approach to remediate stability challenges in power systems with high shares of IBRs and accelerating the integration of GFM technologies for system strength support in power systems.
Experiments have shown that plasma environments can induce discharges in solar arrays. These plasmas simulate the environments found in low earth orbits where current plans call for operation of very large power systems. The discharges could be large enough to couple into the power system and possibly disrupt operations. Here, the general concepts of the discharge mechanism and the techniques of coupling are discussed. Data from both ground and flight experiments are reviewed to obtain an expected basis for the interactions. These concepts were applied to the Space Station solar array and distribution system as an example of the large space power system. The effect of discharges was found to be a function of the discharge site. For most sites in the array discharges would not seriously impact performance. One location at the negative end of the array was identified as a position where discharges could couple to charge stored in system capacitors. This latter case could impact performance.