PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 17, 2026Energies0 citationsOpen Access

An Axial Parallel Memory Machine with DC-Bias Flux-Adjustment Capability

View Full Paper
YZYanwen ZhengWolong Electric Group Hangzhou Research Institute (China)YSYuanyuan ShanWolong Electric Group Hangzhou Research Institute (China)LQLing QinTongji University

Key Points

  • The aim is to improve the performance and reliability of axial parallel memory machines by reducing magnetic interference.
  • Proposed an axial parallel memory machine (DCB-AXMM) with DC-bias-controlled variable-flux capability.
  • Utilized a two-step optimization framework based on a genetic algorithm (GA).
  • Conducted comprehensive non-linear finite element analysis (FEA) to validate the design.
  • Achieved a 21.8% average torque reduction from 2.2 Nm at full magnetization to 1.72 Nm at zero magnetization.
  • Maintained a robust 1.5-times overload capability.
  • The proposed design effectively minimizes magnetic interference and maximizes performance.

Abstract

Conventional memory machines often suffer from magnetic interference between high-coercive-force (HCF) and low-coercive-force (LCF) permanent magnets, which unintentionally alters the magnetization state and limits overload capability. To address this challenge, this paper proposes a novel axial parallel memory machine (DCB-AXMM) featuring a DC-bias-controlled variable-flux capability. Instead of a conventional structure, the proposed machine employs an axially segmented topology to spatially isolate the excitation sources, effectively shielding the LCF PMs from HCF PM interference and armature reaction. Furthermore, integrated windings are utilized to perform both armature excitation and pulse magnetization, thereby enhancing the overall space utilization. The flux-regulating mechanism is theoretically elucidated using a piecewise linear hysteresis model. To maximize electromagnetic performance, a two-step optimization framework based on a genetic algorithm (GA) is implemented. Comprehensive non-linear finite element analysis (FEA) is conducted to validate the proposed design. Quantitative results demonstrate that the DCB-AXMM achieves a wide flux regulation range, characterized by a 21.8% average torque reduction from 2.2 Nm at full magnetization to 1.72 Nm at zero magnetization, while maintaining a robust 1.5-times overload capability. These measurable outcomes confirm the topology’s effectiveness and reliability for high-performance variable-flux applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/6a095b1b7880e6d24efe0d73https://doi.org/10.3390/en19102368
Ask AI
Helpful
Bookmark
Share
View Full Paper